Motion monitoring methods and systems

ABSTRACT

A motion monitoring method ( 500 ) is provided, which includes: obtaining a movement signal of a user during motion, wherein the movement signal includes at least an electromyographic signal or an attitude signal ( 510 ); and monitoring a movement of the user during motion based at least on feature information corresponding to the electromyographic signal or the feature information corresponding to the attitude signal ( 520 ).

CROSS REFERENCE TO RELATED APPLICATIONS

This application is a Continuation of International Patent ApplicationNo. PCT/CN2021/081931, filed on Mar. 19, 2021, the entire contents ofeach of which are hereby incorporated by references.

TECHNOLOGY FIELD

The present disclosure relates to a technical field of wearable devices,and in particular, to a motion monitoring method and system.

BACKGROUND

With people concerned about scientific exercise and physical health,motion monitoring devices are developing tremendously. At present, themotion monitoring devices mainly monitor some of the physiologicalparameter information (e.g., heart rate, body temperature, stepfrequency, blood oxygen, etc.) of a user during motion, but cannotaccurately monitor user's movement and provide feedback on the movement.In practical scenarios, a process of monitoring and feeding the users'movement back often requires the participation of live professionals.For example, the user in the fitness scenario can only correct theirmovement under the guidance of a fitness instructor.

Therefore, it is desirable to provide a motion monitoring device thatcan guide a person's motion and thereby helping the user achieve morescientifically exact exercises.

SUMMARY

According to the embodiments of the present disclosure, a motionmonitoring method is provided, including: obtaining a movement signal ofa user during motion, the movement signal including at least anelectromyographic signal or an attitude signal; and monitoring, at leastbased on feature information corresponding to the electromyographicsignal or feature information corresponding to the attitude signal, amovement of the user during motion.

In some embodiments, the monitoring, at least based on featureinformation corresponding to the electromyographic signal or featureinformation corresponding to the attitude signal, a movement of the userduring motion: segmenting, based on the feature informationcorresponding to the electromyographic signal or the feature informationcorresponding to the attitude signal, the movement signal; andmonitoring, based on at least one segment of the movement signal, themovement of the user during motion.

In some embodiments, the feature information corresponding to theelectromyographic signal includes at least frequency information oramplitude information, and the feature information corresponding to theattitude signal includes at least one of an angular velocity direction,an angular velocity value, an acceleration of an angular velocity, anangle, displacement information, and stress.

In some embodiments, the segmenting, based on the feature informationcorresponding to the electromyographic signal or the feature informationcorresponding to the attitude signal, the movement signal includes:determining, based on a time domain window of the electromyographicsignal or the attitude signal, at least one target feature point fromthe time domain window according to a preset condition; and segmenting,based on the at least one target feature point, the movement signal.

In some embodiments, the at least one target feature point includes oneof a movement start point, a movement middle point, and a movement endpoint.

In some embodiments, the preset condition includes a change in theangular velocity direction corresponding to the attitude signal, theangular velocity corresponding to the attitude signal being greater thanor equal to an angular velocity threshold, a changed value of theangular velocity value corresponding to the attitude signal being anextreme value, the angle corresponding to the attitude signal reachingan angular threshold, and the amplitude information corresponding to theelectromyographic signal being greater than or equal to one or moreelectromyographic thresholds.

In some embodiments, the preset condition further includes theacceleration of the angular velocity corresponding to the attitudesignal being continuously greater than or equal to an accelerationthreshold of the angular velocity for a first specific time range.

In some embodiments, the preset condition further includes an amplitudecorresponding to the electromyographic signal being continuously greaterthan the one or more electromyographic thresholds for a second specifictime range.

In some embodiments, the monitoring, at least based on featureinformation corresponding to the electromyographic signal or featureinformation corresponding to an attitude signal, a movement of the userduring motion includes: pre-processing the electromyographic signal in afrequency domain or a time domain; obtaining, based on the pre-processedelectromyographic signal, the feature information corresponding to theelectromyographic signal; and monitoring, according to the featureinformation corresponding to the electromyographic signal or the featureinformation corresponding to the attitude signal, the movement of theuser during motion.

In some embodiments, the pre-processing of the electromyographic signalin a frequency domain or a time domain includes: filtering theelectromyographic signal to select components of the electromyographicsignal in a specific frequency range in the frequency domain.

In some embodiments, the pre-processing of the electromyographic signalin a frequency domain or a time domain includes: performing a signalcorrection process on the electromyographic signal in the time domain.

In some embodiments, the performing a signal correction processing onthe electromyographic signal in the time domain includes: determining asingularity in the electromyographic signal, wherein the singularitycorresponds to an abrupt signal of the electromyographic signal; andperforming the signal correction processing on the singularity in theelectromyographic signal.

In some embodiments, the performing the signal correction processing onthe singularity in the electromyographic signal includes removing thesingularity, or correcting the singularity according to a signal aroundthe singularity.

In some embodiments, the singularity includes a burr signal, thedetermining the singularity in the electromyographic signal includes:selecting, based on the time domain window of the electromyographicsignal, different time windows from the time domain window of theelectromyographic signal, wherein the different time windowsrespectively cover different time ranges; and determining, based on thefeature information corresponding to the electromyographic signal in thedifferent time windows, the burr signal.

In some embodiments, the method further includes determining, based onthe attitude signal, the feature information corresponding to theattitude signal, wherein the attitude signal includes coordinateinformation in at least one original coordinate system; determining,based on the attitude signal, the feature information corresponding tothe attitude signal includes: obtaining a target coordinate system and aconversion relationship between the target coordinate system and the atleast one original coordinate system; converting, based on theconversion relationship, the coordinate information in the at least oneoriginal coordinate system to coordinate information in the targetcoordinate system; and determining, based on the coordinate informationin the target coordinate system, the feature information correspondingto the attitude signal.

In some embodiments, the attitude signal includes coordinate informationgenerated by at least two sensors, the at least two sensors are locatedat different motion parts of the user and correspond to differentoriginal coordinate systems, the determining, based on the attitudesignal, the feature information corresponding to the attitude signalincludes: determining feature information corresponding to each of theat least two sensors based on the conversion relationship betweendifferent original coordinate systems and the target coordinate system;and determining, based on the feature information respectivelycorresponding to the at least two sensors, a relative motion between themotion parts of the user.

In some embodiments, the conversion relationship between the at leastone original coordinate system and the target coordinate system isobtained by a calibration process, the calibration process includes:constructing a specific coordinate system, the specific coordinatesystem being related to an orientation of the user during thecalibration process; obtaining first coordinate information of the atleast one original coordinate system when the user is in a first pose;obtaining second coordinate information of the at least one originalcoordinate system when the user is in a second pose; and determining theconversion relationship between the at least one original coordinatesystem and the specific coordinate system according to the firstcoordinate information, the second coordinate information, and thespecific coordinate system.

In some embodiments, the calibration process further includes: obtaininga conversion relationship between the specific coordinate system and thetarget coordinate system; and determining, according to the conversionrelationship between the at least one original coordinate system and thespecific coordinate system as well as the conversion relationshipbetween the specific coordinate system and target coordinate system, theconversion relationship between the at least one original coordinatesystem and the target coordinate system.

In some embodiments, the target coordinate system changes as the user'sorientation changes.

According to another aspect of the present disclosure, a method oftraining a movement recognition model is provided, including: obtainingsample information, the sample information including a movement signalof a user during motion, the movement signal including at least featureinformation corresponding to an electromyographic signal and featureinformation corresponding to an attitude signal; and training, based onthe sample information, the movement recognition model.

According to another aspect of the present disclosure, a motionmonitoring and feedback method is provided, including: obtaining amovement signal of a user during motion, wherein the movement signalincludes at least an electromyographic signal and an attitude signal;and monitoring, based on feature information corresponding to theelectromyographic signal and feature information corresponding to theattitude signal, a movement of a user by a movement recognition model,and providing, based on an output of the movement recognition model, amovement feedback.

In some embodiments, the movement recognition model includes a trainedmachine learning model or a preset model.

In some embodiments, the movement feedback includes at least one ofsending a prompt message, stimulating a movement part of the user, andoutputting a motion record of the user during motion.

BRIEF DESCRIPTION OF THE DRAWINGS

The present disclosure is further illustrated in terms of exemplaryembodiments, and these exemplary embodiments are described in detailwith reference to the drawings. These embodiments are not limiting. Inthese embodiments, the same number indicates the same structure,wherein:

FIG. 1 is a schematic diagram illustrating an application scenario of amotion monitoring system according to some embodiments of the presentdisclosure;

FIG. 2 is a schematic diagram illustrating an exemplary hardware and/orsoftware of a wearable device according to some embodiments of thepresent disclosure;

FIG. 3 is a schematic diagram illustrating an exemplary hardware and/orsoftware of a computing device according to some embodiments of thepresent disclosure;

FIG. 4 is a structure diagram of an exemplary wearable device accordingto some embodiments of the present disclosure;

FIG. 5 is a flowchart of an exemplary motion monitoring method accordingto some embodiments of the present disclosure;

FIG. 6 is a flowchart of an exemplary process for monitoring a movementof user motion according to some embodiments of the present disclosure;

FIG. 7 is a flowchart of an exemplary process for segmenting a movementsignal according to some embodiments of the present disclosure;

FIG. 8 is a diagram illustrating exemplary normalized results ofsegmenting a movement according to some embodiments of the presentdisclosure;

FIG. 9 is a flowchart of an exemplary process for pre-processing anelectromyographic signal according to some embodiments of the presentdisclosure;

FIG. 10 is a flow chart illustrating an exemplary burr signal accordingto some embodiments of the present disclosure;

FIG. 11 is a flowchart of an exemplary process for determining featureinformation corresponding to an attitude signal according to someembodiments of the present disclosure;

FIG. 12 is a flowchart of an exemplary process for determining relativemotion between different motion parts of a user according to someembodiments of the present disclosure;

FIG. 13 is a flowchart of an exemplary process for determining aconversion relationship between an original coordinate system to aparticular coordinate system according to some embodiments of thepresent disclosure;

FIG. 14 is a flowchart of an exemplary process for determining aconversion relationship between an original coordinate system and atarget coordinate system according to some embodiments of the presentdisclosure;

FIG. 15A is an exemplary vector coordinate diagram illustrating Eulerangle data in an original coordinate system at a position of a small armof a human body according to some embodiments of the present disclosure;

FIG. 15B is an exemplary vector coordinate diagram illustrating Eulerangle data in another original coordinate system for a position of asmall arm of ae human body according to some embodiments of the presentdisclosure;

FIG. 16A is an exemplary vector coordinate diagram of Euler angle datain a target coordinate system at a position of a small arm of a humanbody according to some embodiments of the present disclosure;

FIG. 16B is an exemplary vector coordinate diagram of Euler angle datain a target coordinate system at another location of a small arm of ahuman body according to some embodiments of the present disclosure;

FIG. 17 is an exemplary vector coordinate diagram of Euler angle data ina target coordinate system of a multi-sensor according to someembodiments of the present disclosure;

FIG. 18A is a diagram illustrating exemplary results of an originalangular velocity according to some embodiments of the presentdisclosure;

FIG. 18B is a diagram illustrating exemplary results of an angularvelocity after filtering processing according to some embodiments of thepresent disclosure;

FIG. 19 is a flowchart illustrating an exemplary motion monitoring andfeedback method according to some embodiments of the present disclosure;and

FIG. 20 is a flowchart illustrating exemplary process for model trainingaccording to some embodiments of the present disclosure.

DETAILED DESCRIPTION

To more clearly illustrate the technical solutions related to theembodiments of the present disclosure, a brief introduction of thedrawings referred to the description of the embodiments is providedbelow. Obviously, the accompanying drawing in the following descriptionis merely some examples or embodiments of the present disclosure, forthose skilled in the art, the present disclosure may further be appliedin other similar situations according to the drawings without anycreative effort. Unless obviously obtained from the context or thecontext illustrates otherwise, the same numeral in the drawings refersto the same structure or operation.

It will be understood that the term “system,” “device,” “unit,” and/or“module” used herein are one method to distinguish different components,elements, parts, sections or assemblies of different levels in ascendingorder. However, if other words may achieve the same purpose, the wordsmay be replaced by other expressions.

As used in the disclosure and the appended claims, the singular forms“a,” “an,” and “the” include plural referents unless the content clearlydictates otherwise. Generally speaking, the terms “comprise,”“comprises,” and/or “comprising,” “include,” “includes,” and/or“including,” only imply that the clearly identified steps and elementsare included, these steps and elements may not constitute an exclusivelist, and the method or device may further include other steps orelements.

Flowcharts are used throughout the present disclosure to illustrate theoperations performed by the system according to embodiments of thepresent disclosure. It should be understood that the preceding orfollowing operations are not necessarily performed in precise order.Instead, the individual steps may be processed in reverse order orsimultaneously. Other operations may be added to these processes or astep or steps of operations may be removed from these processes.

According to the present disclosure, a motion monitoring system isprovided, which may obtain a movement signal of a user during motion.The movement signal includes at least an electromyographic signal, anattitude signal, an electro-cardio graphic signal, a respiratory ratesignal, and the like. The motion monitoring system may monitor amovement of the user during motion based at least on feature informationcorresponding to the electromyographic signal or the feature informationcorresponding to an attitude signal. For example, the system maydetermine the type of movement of the user, the number of movement, themovement quality, movement time, or information of physiologicalparameters of the user when performing the movement through frequencyinformation and amplitude information corresponding to theelectromyographic signal, an angular velocity, an angular velocitydirection and an angular velocity value of the angular velocity, anangle, displacement information, and stress, etc. corresponding to theattitude signal. In some embodiments, the motion monitoring system mayfurther generate feedback to a user's fitness movement according toanalysis results of the user's fitness movement to provide guidance touser's fitness. For example, when the user's fitness movement is notstandard, the motion monitoring system can send a prompt message to theuser (e.g., a voice prompt, a vibration prompt, current stimulation,etc.). The motion monitoring system may be applied to a wearable device(e.g., clothing, a wrist guard, a helmet), a medical testing device(e.g., an electromyography tester), a fitness device, etc. The motionmonitoring system may accurately monitor and provide feedback on auser's movement by obtaining the movement signal of the user duringmotion without professional participation, which can improve the user'sfitness efficiency and reduce a cost of the user fitness.

FIG. 1 is a schematic diagram illustrating an application scenario of amotion monitoring system according to some embodiments of the presentdisclosure. As shown in FIG. 1 , the motion monitoring system 100 mayinclude a processing device 110, a network 120, a wearable device 130,and a mobile terminal device 140. The motion monitoring system 100 mayobtain a movement signal (e.g., an electromyographic signal, an attitudesignal, an electro-cardio signal, a respiratory rate signal, etc.)representing a movement of user motion, and may monitor and providefeedback on the movement of the user during motion according to a user'smovement signal.

For example, the motion monitoring system 100 may monitor and providefeedback on the movement of the user during fitness. When the user wearsthe wearable device 130 for fitness, the wearable device 130 may obtainthe user's movement signal. The processing device 110 or a mobileterminal device may receive and analyze the user's movement signal todetermine whether the user's fitness movement is standard, therebymonitoring the user's movement. Specifically, the monitoring of theuser's movement may include determining a type of movement, a count ofmovement, quality of the movement, and timing of the movement, orinformation about the physiological parameters of the user at the timethe movement is performed. Further, the motion monitoring system 100 maygenerate feedback on the user's fitness movement according to theanalysis results of the user's fitness movement to provide guidance tothe user.

Further, for example, the motion monitoring system 100 may monitor andprovide feedback on the user's movement while running. For example, whenthe user wears the wearable device 130 for running exercise, the motionmonitoring system 100 may monitor whether the user's running movement isstandard and whether the running time meets a health standard. When auser's running time is too long or a running movement is incorrect, thefitness device may provide motion state to the user to prompt the userto adjust the running movement or the running time.

In some embodiments, the processing device 110 may be configured toprocess information and/or data related to the user's movement. Forexample, the processing device 110 may receive the movement signal ofthe user (e.g., an electromyographic signal, an attitude signal, anelectro-cardio signal, a respiratory rate signal, etc.) and furtherextract the feature information corresponding to the movement signal(e.g., feature information corresponding to the electromyographic signalin the movement signal, the feature information corresponding to theattitude signal). In some embodiments, the processing device 110 mayperform a specific signal processing, such as signal segmentation,signal pre-processing (e.g., signal correction processing, filteringprocessing, etc.), etc., on the electromyographic signal or the attitudesignal obtained by the wearable device 130. In some embodiments, theprocessing device 110 may further determine whether the user movement iscorrect based on the user's movement signal. For example, the processingdevice 110 may determine whether the user movement is correct based onthe feature information corresponding to the electromyographic signal(e.g., amplitude information, frequency information, etc.). For anotherexample, the processing device 110 may determine whether the usermovement is correct based on the feature information corresponding tothe attitude signal (e.g., an angular velocity, a direction of angularvelocity, an acceleration of angular velocity, an angle, displacementinformation, a stress, etc.). Further, for example, the processingdevice 110 may determine whether the user movement is correct based onthe feature information corresponding to the electromyographic signaland the feature information corresponding to the attitude signal. Insome embodiments, the processing device 110 may further determinewhether information of physiological parameters of the user duringmotion meets the health standard. In some embodiments, the processingdevice 110 may further send a corresponding instruction configured tofeed the user's movement back. For example, when the user is running andthe motion monitoring system 100 monitors that the user's running timeis too long, the processing device 110 may send the instruction to themobile terminal device 140 to prompt the user to adjust the runningtime. It should be noted that the feature information corresponding tothe attitude signal is not limited to above angular velocity, thedirection of angular velocity, the acceleration of angular velocity, theangle, the displacement information, and the stress, etc., but can alsobe other feature information. For example, when an attitude sensor is astrain gauge sensor, a bending angle and a bending direction at a user'sjoint may be obtained by measuring the resistance in a strain gaugesensor that varies with a stretch length.

In some embodiments, the processing device 110 may be local or remote.For example, the processing device 110 may access information and/ormaterials stored in the wearable device 130 and/or the mobile terminaldevice 140 through the network 120. In some embodiments, the processingdevice 110 may be directly connected to the wearable device 130 and/orthe mobile terminal device 140 to access the information and/ormaterials stored therein. For example, the processing device 110 may belocated in the wearable device 130 and implement the informationinteract with the mobile terminal device 140 through the network 120.Further, for example, the processing device 110 may be located in themobile terminal device 140 and implement the information interact withthe wearable device 130 through a network. In some embodiments, theprocessing device 110 may be executed on a cloud platform. For example,the cloud platform may include one of a private cloud, a public cloud, ahybrid cloud, a community cloud, a decentralized cloud, an internalcloud, or any combination thereof.

In some embodiments, the processing device 110 may process data and/orinformation related to motion monitoring to perform one or more of thefunctions described in the present disclosure. In some embodiments, theprocessing device 110 may obtain the movement signal collected by thewearable device 130 while the user is in motion. In some embodiments,the processing device may send a control instruction to the wearabledevice 130 or the mobile terminal device 140. The control instructionmay control an on/off state of the wearable device 130 and itsrespective sensor, and also control the mobile terminal device 140 tosend a prompt message. In some embodiments, processing device 110 mayinclude one or more sub-processing devices (e.g., a single-coreprocessing device or a multi-core processing device). Merely by way ofexample, the processing device 110 may include a central processing unit(CPU), an application-specific integrated circuit (ASIC), anapplication-specific instruction processor (ASIP), a graphic processingunit (GPU), a physics processing Unit (PPU), a digital signal processor(DSP), a field-programmable gate array (FPGA), an programmable logicdevice (PLD), a controller, a microcontroller unit Reduced InstructionSet Computer (RISC), and a microprocessor, etc. or any combination ofthe above.

The network 120 may facilitate an exchange of data and/or information inthe motion monitoring system 100. In some embodiments, one or morecomponents of the motion monitoring system 100 (e.g., the processingdevice 110, the wearable device 130, the mobile terminal device 140) maysend the data and/or the information to other components of the motionmonitoring system 100 through network 120. For example, the movementsignal collected by the wearable device 130 may be transmitted to theprocessing device 110 through the network 120. For another example,confirmation results regarding the movement signal in the processingdevice 110 may be transmitted to the mobile terminal device 140 throughthe network 120. In some embodiments, the network 120 may be any type ofa wired or wireless network. For example, the network 120 may include acable network, a wired network, a fiber optic network, atelecommunications network, an internal network, an inter-network, aregional network (LAN), a wide area network (WAN), a wireless regionalnetwork (WLAN), a metropolitan area network (MAN), a public switchedtelephone network (PSTN), a Bluetooth™ network, a ZigBee™ network, and anear field communication (NFC) network, or any combination of the above.In some embodiments, the network 120 may include one or more networkentry and exit points. For example, network 120 may include wired orwireless network entry and exit points, such as a base station and/orinter-network exchange points 120-1, 120-2, . . . , through the entryand exit points, one or more components of motion monitoring system 100may connect to the network 120 to exchange the data and/or theinformation.

The wearable device 130 is a garment or device that has a wearablefunction. In some embodiments, the wearable device 130 may include, butis not limited to, an upper garment device 130-1, a pant device 130-2, awrist guard device 130-3, and a shoe 130-4, etc. In some embodiments,wearable device 130 may include a plurality of sensors. The sensors mayobtain various movement signals (e.g., electromyographic signals,attitude signals, temperature information, heart rate, electro-cardiosignals, etc.) from the user during motion. In some embodiments, thesensors may include, but are not limited to, one or more of anelectromyographic sensor, an attitude sensor, a temperature sensor, ahumidity sensor, an electro-cardio sensor, an oxygen saturation sensor,a Hall sensor, a Pico electric sensor, and a rotation sensor, etc. Forexample, an electromyographic sensor may be provided at a human musclelocation (e.g., biceps, triceps, latissimus dorsi, trapezius, etc.) inan upper garment device 130-1, and the electromyographic sensor may fitto user's skin and collect the electromyographic signal from the userduring motion. For example, the upper garment device 130-1 may beprovided with an electro-cardio sensor near the left pectoral muscle ofthe human body, and the electromyographic sensor may collect theelectro-cardio signal of the user. Further, for example, the attitudesensor may be provided at a human body muscle location (e.g., gluteusmaximus, lateral femoris, medial femoris, gastrocnemius, etc.) in apants device 130-2, and the attitude sensor may collect a user'sattitude signal. In some embodiments, the wearable device 130 mayfurther provide feedback on the user's movement. For example, if theuser's movement of a body part during motion does not meet the standard,the electromyographic sensor corresponding to that part may generate astimulation signal (e.g., a current stimulation or a strike signal) toprompt the user.

It should be noted that the wearable device 130 is not limited to theupper garment device 130-1, the pants device 130-2, a wrist guard device130-3, and a shoe device 130-4 shown in FIG. 1 , but may further includea device that are applied to other devices that require motionmonitoring, such as, for example, helmet devices, knee pads, etc., whichwill not be limited herein, and any device that can use the motionmonitoring method contained in the disclosure is within the scope ofprotection of the present disclosure.

In some embodiments, the mobile terminal device 140 may accessinformation or data in the motion monitoring system 100. In someembodiments, the mobile terminal device 140 may receive motion dataprocessed by the processing device 110, and feed motion records backbased on processed motion data. An exemplary feedback manner mayinclude, but are not limited to, a voice prompt, an image prompts, avideo display, and a text prompt, etc. In some embodiments, the user mayobtain movement records during an own movement through the mobileterminal device 140. For example, the mobile terminal device 140 may beconnected to the wearable device 130 through the network 120 (e.g., thewired connection, the wireless connection), and the user may obtain themovement records during the user's movement through the mobile terminaldevice 140, which may be transmitted to the processing device 110through the mobile terminal device 140. In some embodiments, the mobileterminal device 140 may include a mobile device 140-1, a tablet 140-2, alaptop 140-3, etc., or any combination thereof. In some embodiments,mobile device 140-1 may include a cell phone, a smart home device, asmart mobility device, a virtual reality device, an augmented realitydevice, etc., or any combination thereof. In some embodiments, the smarthome device may include a control device of a smart appliance, a smartmonitoring device, a smart TV, a smart camera, etc., or any combinationthereof. In some embodiments, the smart mobility device may include asmart phone, a personal digital assistant (PDA), a gaming device, anavigation device, a POS device, etc., or any combination thereof. Insome embodiments, a virtual reality device and/or an augmented realitydevice may include a virtual reality helmet, virtual reality glasses, avirtual reality eye-mask, an augmented reality helmet, an augmentedreality glasses, and an augmented reality eye-mask, etc., or anycombination thereof.

In some embodiments, the motion monitoring system 100 may furtherinclude a database. The database may store the information (e.g., athreshold condition of an initially set, etc.) and/or the instruction(e.g., a feedback instruction). In some embodiments, the database maystore the information obtained from the wearable device 130 and/or themobile terminal device 140. In some embodiments, the database may storethe information and/or the instruction configured for the processingdevice 110 to execute or use to perform the exemplary methods describedin the present disclosure. In some embodiments, the database may includea mass storage, a removable memory, a volatile read-write memory (e.g.,random access memory RAM), a read-only memory (ROM), etc., or anycombination thereof. In some embodiments, the database may beimplemented on a cloud platform. For example, the cloud platform mayinclude the private cloud, the public cloud, the hybrid cloud, thecommunity cloud, the decentralized cloud, the internal cloud, or anycombination thereof.

In some embodiments, the database may be connected to the network 120 tocommunicate with one or more components of the motion monitoring system100 (e.g., the processing device 110, the wearable device 130, themobile terminal device 140, etc.). The one or more components of themotion monitoring system 100 may access information or instructionstored in the database through the network 120. In some embodiments, thedatabase may be directly connected or communicate with one or morecomponents of the motion monitoring system 100 (e.g., the processingdevice 110, the wearable device 130, the mobile terminal device 140). Insome embodiments, the database may be a part of the processing device110.

FIG. 2 is a schematic diagram illustrating an exemplary hardware and/orsoftware of a wearable device according to some embodiments of thepresent disclosure. As shown in FIG. 2 , the wearable device 130 mayinclude an obtaining module 210, a processing module 220 (also referredto as a processor), a control module 230 (also referred to as a master,MCU, a controller), a communication module 240, a power supply module250, and an input/output module 260.

The obtaining module 210 may be configured to obtain a movement signalof a user during motion. In some embodiments, the obtaining module 210may include a sensor unit, and the sensor unit may be configured toobtain one or more movement signals while the user is in motion. In someembodiments, the sensor unit may include, but is not limited to, one ormore electromyographic sensors, attitude sensors, cardiac sensors,respiration sensors, temperature sensors, humidity sensors, inertialsensors, blood oxygen saturation sensors, Hall sensors, piezoelectricsensors, and rotation sensors, and the like. In some embodiments, themovement signal may include one or more electromyographic signals,attitude signals, cardiac signals, respiratory rates, temperaturesignals, and humidity signals, etc. The sensor unit may be placed atdifferent locations of the wearable device 130 according to a type of amovement signal to be obtained. For example, in some embodiments, theelectromyographic sensor (also referred to as an electrode element) maybe placed at a human muscle location, and the electromyographic sensormay be configured to collect the electromyographic signal of the userduring motion. The electromyographic signal and its correspondingfeature information (e.g., frequency information, amplitude information,etc.) may reflect a state of muscle during a user's movement. Theattitude sensor may be provided at different locations on a human body(e.g., locations of the wearable device 130 corresponding to the torso,limbs, and joints), and the attitude sensor may be configured to capturethe attitude signal of the user during the user's movement. The attitudesignal and its corresponding feature information (e.g., angular velocitydirection, angular velocity value, acceleration value of angularvelocity, angle, displacement information, stress, etc.) may reflect theattitude of the user's movement. The electromyographic sensor may be setat a location on the circumferential side of the human chest, and theelectromyographic sensor may be configured to collect electro cardiodata of the user during motion. The respiration sensor may be arrangedon a circumferential side of the body's chest, and the respirationsensor may be configured to collect respiration data (e.g., respirationrate, respiration amplitude, etc.) from the user during motion. Thetemperature sensor may be configured to collect temperature data (e.g.,a body surface temperature) of the user during motion. The humiditysensor may be configured to collect humidity data of an externalenvironment of the user during motion.

The processing module 220 may process data from the obtaining module210, the control module 230, the communication module 240, the powersupply module 250, and/or the input/output module 260. For example, theprocessing module 220 may process the movement signal of the user duringa process of motion from the obtaining module 210. In some embodiments,the processing module 220 may pre-process the movement signal (e.g., theelectromyographic signal, the attitude signal) obtained by the obtainingmodule 210. For example, the processing module 220 segments theelectromyographic signal or the attitude signal of the user duringmotion. For another example, the processing module 220 may perform apre-processing (e.g., a filtering processing, a signal correctionprocessing) on the electromyographic signal of the user during motion toimprove quality of the electromyographic signal. Further, for example,the processing module 220 may determine the feature informationcorresponding to the attitude signal based on a user's attitude signalduring motion. In some embodiments, the processing module 220 mayprocess an instruction or operation from an input/output module 260. Insome embodiments, processed data may be stored in a memory or a harddisk. In some embodiments, the processing module 220 may transmit itsprocessed data to one or more components in the motion monitoring system100 through the communication module 240 or the network 120. Forexample, the processing module 220 may send monitoring results of theuser during motion to the control module 230, which may executesubsequent operations or instructions according to motion determinationresults.

The control module 230 may be connected to other modules in the wearabledevice 130. In some embodiments, the control module 230 may control anoperation state of other modules (e.g., the communication module 240,the power supply module 250, the input/output module 260) in thewearable device 130. For example, the control module 230 may control apower supply state (e.g., a normal mode, a power saving mode), powersupply time, and the like, of the power supply module 250. Whenremaining power of the power supply module 250 reaches a certainthreshold (e.g., 10%) or less, the control module 230 may control thepower supply module 250 to enter a power saving mode or send a promptmessage about replenishment of power. For another example, the controlmodule 230 may control the input/output module 260 based on user'smovement determination results, and further control the mobile terminaldevice 140 to send feedback results of the user's movement. When thereis a problem with the user's movement (e.g., movement not meeting thestandard), the control module 230 may control the input/output module260 to control the mobile terminal device 140 to provide feedback to theuser, allowing the user to understand own motion movement in real timeand make some adjustments. In some embodiments, the control module 230may also control one or more sensors or other modules in the obtainingmodule 210 to provide feedback to the human body. For example, when amuscle of the user is exercising too strong during motion, the controlmodule 230 may control an electrode module at a location of the muscleto stimulate the user to prompt the user to adjust the movement in time.

In some embodiments, the communication module 240 may be configured foran exchange of information or data. In some embodiments, thecommunication module 240 may be configured for communication betweencomponents (e.g., the obtaining module 210, the processing module 220,the control module 230, the power supply module 250, the input/outputmodule 260) within a wearable device 130. For example, the obtainingmodule 210 may send a movement signal (e.g., the electromyographicsignal, the attitude signal, etc.) to the communication module 240, andthe communication module 240 may send the movement signal to theprocessing module 220. For example, the communication module 240 maysend state information (e.g., a switch state) of the wearable device 130to the processing device 110, and the processing device 110 may monitorthe wearable device 130 based on the state information. Thecommunication module 240 may employ wired, wireless, and hybridwired/wireless technologies. The wired technology may be based on one ormore combinations of fiber optic cables such as metallic cables, hybridcables, fiber optic cables, etc. The wireless technologies may includeBluetooth (Bluetooth™), wireless network (Wi-Fi), purple bee (ZigBee™),Near Field Communication (NFC), Radio Frequency Identification (RFID),cellular networks (including GSM, CDMA, 3G, 4G, 5G, etc.), andcellular-based Narrow Band Internet of Things (NBIoT), etc. In someembodiments, the communication module 240 may use one or more codingmethods to encode transmitted information, for example, the codingmethods may include phase coding, non-zeroing coding, differentialManchester coding, and the like. In some embodiments, the communicationmodule 240 may select different transmission and encoding methodsaccording to a type of data or a type of network to be transmitted. Insome embodiments, the communication module 240 may include one or morecommunication interfaces for different communication methods. In someembodiments, illustrated other modules of the motion monitoring system100 may be dispersed on a plurality of devices, in this case, each of aplurality of other modules may each include one or more communicationmodules 240 for an inter-module information transmission. In someembodiments, the communication module 240 may include a receiver and atransmitter. In other embodiments, the communication module 240 may be atransceiver.

In some embodiments, the power supply module 250 may provide power toother components in the motion monitoring system 100 (e.g., theobtaining module 210, the processing module 220, the control module 230,the communication module 240, and the input/output module 260). Thepower supply module 250 may receive the control signal from theprocessing module 220 to control a power output of the wearable device130. For example, if the wearable device 130 does not receive anyoperation (e.g., no movement signal is detected by the obtaining module210) for a certain period (e.g., 1 s, 2 s, 3 s, or 4 s), the powersupply module 250 may supply power to the memory merely, putting thewearable device 130 into a standby mode. For example, if the wearabledevice 130 does not receive any operation (e.g., no movement signal isdetected by the obtaining module 210) for a certain period (e.g., 1 s, 2s, 3 s, or 4 s), the power supply module 250 may disconnect power toother components and the data in the motion monitoring system 100 may betransmitted to a hard disk, putting the wearable device 130 into thestandby mode or a sleeping mode. In some embodiments, the power supplymodule 250 may include at least one battery. The battery may include oneor more combinations of a dry cell, a lead battery, a lithium battery, asolar cell, a wind energy generation battery, a mechanical energygeneration battery, a thermal energy generation battery, etc. Lightenergy maybe converted into electrical energy by the solar battery andstored in the power supply module 250. Wind energy may be converted intothe electrical energy by the wind power generation battery and stored inthe power supply module 250. Mechanical energy may be converted into theelectrical energy by the mechanical energy generation battery and storedin the power supply module 250. The solar cell may include a siliconsolar cell, a thin film solar cell, a nanocrystalline chemical solarcell, a fuel sensitized solar cell, and a plastic solar cell, etc. Thesolar cell may be distributed on the wearable device 130 in a form ofpanel. A user's body temperature may be converted into the electricalenergy by the thermal power cell and stored in the power supply module250. In some embodiments, the processing module 220 may send the controlsignal to the power supply module 250 when the power supply module 250is less than a power threshold (e.g., 10% of the total power). Thecontrol signal may include information that the power supply module 250is low on power. In some embodiments, the power supply module 250 mayinclude a backup power source. In some embodiments, the power supplymodule 250 may further include a charging interface. For example, thepower supply module 250 may be temporarily charged by using anelectronic device (e.g., a cell phone, a tablet computer) or arechargeable battery carried by the user to temporarily charge the powersupply module 250 in an emergency (e.g., the power supply module 250 isat zero power and an external power system is out of power).

The input/output module 260 may obtain, transmit, and send a signal. Theinput/output module 260 may connect or communicate with other componentsin the motion monitoring system 100. The other components in the motionmonitoring system 100 may be connected or communicated through theinput/output module 260. The input/output module 260 may be a wired USBinterface, a serial communication interface, a parallel communicationport, or a wireless Bluetooth, infrared-frequency identification,radio-frequency identification (RFID), WLAN Authentication and PrivacyInfrastructure (WAPI), General Packet Radio Service (GPRS), CodeDivision Multiple Access (CDMA), or any combination thereof. In someembodiments, the input/output module 260 may be connected to the network120 and obtain the information through the network 120. For example, theinput/output module 260 may obtain the movement signal from theobtaining module 210 of the user during motion and output user movementinformation through the network 120 or the communication module 240. Insome embodiments, the input/output module 260 may include VCC, GND,RS-232, RS-485 (e.g., RS485-A, RS485-B), and a universal networkinterface, or any combination thereof. In some embodiments, theinput/output module 260 may transmit obtained user motion information,through the network 120, to the obtaining module 210. The encodingmethods may include the phase coding, the non-zeroing system encoding,the differential Manchester encoding, etc., or any combination thereof.

It should be understood that the system and its modules shown in FIG. 2may be implemented by using a plurality of methods. For example, in someembodiments, the system and its modules may be implemented by hardware,software, or a combination of software and hardware. In particular, ahardware portion may be implemented by using dedicated logic. A softwareportion may be stored in memory and executed by an appropriateinstruction execution system, such as a microprocessor or dedicateddesign hardware. Those skilled in the art may understand that the abovemethods and the system can be implemented by using a computer executableinstruction and/or contained in a processor control code, for example,such encoding provided on a carrier medium such as a disk, CD orDVD-ROM, a programmable memory such as a read-only memory (firmware), ora data carrier such as an optical or electronic signal carrier. Thesystem and its modules in one or more embodiments of the presentdisclosure may be implemented by a hardware circuit, e.g., ultra-largescale integrated circuit or gate array, a semiconductor such as a logicchip, a transistor, etc., or a programmable hardcore device such as afield programmable gate array, a programmable logic device, etc.,implemented by software executed by various types of processors, orimplemented by a combination of above hardware circuit and software(e.g., firmware).

It should be noted that the above description of the motion monitoringsystem and its modules is merely for descriptive convenience and doesnot limit one or more embodiments of the present disclosure within thescope of the embodiments. Understandably, for those skilled in the art,after understanding a principle of the system, they may make anycombination of the modules, or to form a sub-system to connect withother modules, or to omit one or more modules thereof, without departingfrom this principle. For example, the obtaining module 210 and theprocessing module 220 may be one module that may have a function ofobtaining and processing the user movement signal. Another example isthat the processing module 220 may not be provided in the wearabledevice 130, but integrated in the processing device 110. Variations suchas these are within the scope of protection of one or more embodimentsof the present disclosure.

FIG. 3 is a schematic diagram illustrating an exemplary hardware and/orsoftware of a computing device according to some embodiments of thepresent disclosure. In some embodiments, the processing device 110and/or the mobile terminal device 140 may be implemented on a computingdevice 300. As shown in FIG. 3 , the computing device 300 may include aninternal communication bus 310, a processor 320, a read-only memory 330,a random memory 340, a communication port 350, an input/output interface360, a hard disk 370, and a user interface 380.

The internal communication bus 310 may enable data communication betweencomponents in the computing device 300. For example, the processor 320may send data to other hardware such as a memory or the input/outputinterface 360 through the internal communication bus 310. In someembodiments, the internal communication bus 310 may be an industrystandard architecture (ISA) bus, an extended industry standardarchitecture (EISA) bus, a video electronics standard architecture(VESA) bus, and a peripheral component interconnect (PCI) bus, etc. Insome embodiments, the internal communication bus 310 may be configuredto connect various modules (e.g., obtaining module 210, processingmodule 220, control module 230, communication module 240, input andoutput module 260) of the motion monitoring system 100 shown in FIG. 1 .

The processor 320 may execute a computing instruction (a program code)and perform functions of the motion monitoring system 100 described inthe present disclosure. The computing instruction may include a program,an object, a component, a data structure, process, modules, andfunctions (the functions refer to specific functions described in thepresent disclosure). For example, processor 320 may process the obtainedmovement signal (e.g., the electromyographic signal, the attitudesignal) of a user during motion from the wearable device 130 or/and themobile terminal device 140 of the motion monitoring system 100, andmonitor the movement of the user during motion based on the movementsignal during motion. In some embodiments, the processor 320 may includea microcontroller, a microprocessor, a reduced instruction set computer(RISC), an application-specific integrated circuit (ASIC), anapplication-specific instruction-set processor (ASIP), a centralprocessing unit (CPU), a graphics processing unit (GPU), a physicalprocessing unit (PPU), a microcontroller unit, a digital signalprocessor (DSP), a field Programmable Gate Array (FPGA), an AdvancedRISC Machine (ARM), a programmable logic device, and any circuit andprocessor capable of performing one or more functions, or anycombination thereof. For illustrative purposes only, the computingdevice 300 in FIG. 3 depicts only one processor, but it should be notedthat the computing device 300 in the present disclosure may furtherinclude a plurality of processors.

A memory of computing device 300 (e.g., a read-only memory (ROM) 330, aRandom Access Memory (RAM) 340, a hard disk 370, etc.) may storedata/information obtained from any other components of the motionmonitoring system 100. In some embodiments, the memory of the computingdevice 300 may be located in the wearable device 130 or the processingdevice 110. An exemplary ROM may include a mask ROM (MROM), aprogrammable ROM (PROM), an erasable programmable ROM (PEROM), anelectrically erasable programmable ROM (EEPROM), a compact disk ROM(CD-ROM), and a digital versatile disk ROM, etc. An exemplary RAM mayinclude a dynamic RAM (DRAM), a double-rate synchronous dynamic RAM (DDRSDRAM), a static RAM (SRAM), a thyristor RAM (T-RAM), and azero-capacitor RAM (Z-RAM), etc.

The input/output interface 360 may input or output signals, data, orinformation. In some embodiments, the input/output interface 360 mayenable a user to interact with the motion monitoring system 100. Forexample, the input/output interface 360 may include a communicationmodule 240 to enable the communication function of the motion monitoringsystem 100. In some embodiments, the input/output interface 360 mayinclude an input device and an output device. Exemplary input devicesmay include a keyboard, a mouse, a touch screen, and a microphone, etc.,or any combination thereof. Exemplary output devices may include adisplay device, a loudspeaker, a printer, a projector, etc., or anycombination thereof. Example display devices may include a liquidcrystal display (LCD), a light-emitting diode (LED)-based display, aflat panel display, a curved display, a television device, a cathode raytubes (CRT), etc., or any combination thereof. The communication port350 may be connected to a network for data communication. Connection maybe a wired connection, a wireless connection, or a combination of both.The wired connection may include a cable, a fiber optic cable, ortelephone line, or any combination thereof. The wireless connection mayinclude Bluetooth™, Wi-Fi, WiMAX, WLAN, ZigBee™, a mobile network (e.g.,3G, 4G, or 5G, etc.), or any combination thereof. In some embodiments,the communication port 350 may be a standard port, such as RS232, RS485,etc. In some embodiments, the communication port 350 may be a speciallydesigned port.

The hard disk 370 may be configured to store the information and thedata generated by or received from the processing device 110. Forexample, the hard disk 370 may store confirmation information of a user.In some embodiments, the hard disk 370 may include a hard disk drive(HDD), a solid-state drive (SSD), or a hybrid hard disk (HHD), etc. Insome embodiments, the hard disk 370 may be provided in the processingdevice 110 or in the wearable device 130. The user interface 380 mayenable an interact and information exchange between the computing device300 and the user. In some embodiments, the user interface 380 may beconfigured to present motion recordings generated by the motionmonitoring system 100 to the user. In some embodiments, the userinterface 380 may include a physical display such as a display withspeakers, an LCD display, an LED display, an OLED display, an electronicink display (E-Ink), etc.

FIG. 4 is a structure diagram of an exemplary wearable device accordingto some embodiments of the present disclosure. To further describe thewearable device, the upper garment is illustrated as an example, asshown in FIG. 4 . The wearable device 400 may include an upper garment410. The upper garment 410 may include an upper garment substrate 4110,at least one upper garment processing module 4120, at least one uppergarment feedback module 4130, at least one upper garment obtainingmodule 4140, etc. The upper garment substrate 4110 may refer to clotheworn on an upper body of a human body. In some embodiments, the uppergarment substrate 4110 may include a short sleeve T-shirt, a long sleeveT-shirt, a shirt, and a jacket, etc. The at least one upper garmentprocessing module 4120, the at least one upper garment obtaining module4140 may be located in areas of the upper garment substrate 4110 thatfit to different parts of the human body. The at least one upper garmentfeedback module 4130 may be located at any location on the upper garmentsubstrate 4110, and the at least one upper garment feedback module 4130may be configured to provide feedback on information about a user'supper body movement state. Exemplary feedback manners may include, butare not limited to, voice prompts, text prompts, pressure prompts,electrical stimulation, etc. In some embodiments, the at least one uppergarment obtaining module 4140 may include, but is not limited to, one ormore of an attitude sensor, an electro-cardio sensor, anelectromyographic sensor, a temperature sensor, a humidity sensor, aninertial sensor, an acid-base sensor, an acoustic transducer, and etc.The sensor(s) in the upper garment obtaining module 4140 may be placedat different locations on user's body according to a signal to bemeasured. For example, when the attitude sensor is configured to obtainthe attitude signal of a user during motion, the attitude sensor can beplaced in the upper garment substrate 4110 at a location correspondingto the human torso, arms, and joints. For another example, when theelectromyographic sensor is configured to obtain Electromyographicsignal of the user during motion, the Electromyographic sensor may belocated near the muscles to be measured. In some embodiments, theattitude sensor may include, but is not limited to, an accelerationtriaxial sensor, an angular velocity tri-axial sensor, a magneticsensor, etc., or any combination thereof. For example, an attitudesensor may include an acceleration triaxial sensor, an angular velocitytriaxial sensor. In some embodiments, an attitude sensor may furtherinclude a strain gauge sensor. A strain gauge sensor may be a sensorbased on strain generated by deformation of an object to be measuredcaused by a force. In some embodiments, the strain gauge sensor mayinclude, but is not limited to, one or more of a strain-gauge forcesensor, a strain-gauge pressure sensor, a strain-gauge torque sensor, astrain-gauge displacement sensor, a strain-gauge acceleration sensor,etc. For example, the strain gauge sensor may be arranged at a jointlocation of the user, and a bending angle and a bending direction at theuser's joint can be obtained based on the resistance in the strain gaugesensor that varies with a stretch length at the joint. It should beunderstood that the upper garment 410 may include other modules, such asa power supply module, a communication module, an input/output module,and etc., in addition to the upper garment substrate 4110, the uppergarment processing module 4120, the upper garment feedback module 4130,and the upper garment obtaining module 4140 described above. The uppergarment processing module 4120 is similar to the processing module 220shown in FIG. 2 , and the upper garment obtaining module 4140 is similarto the obtaining module 210 shown in FIG. 2 . Specific descriptionsregarding various modules in the upper garment 410 may be found in FIG.2 and its relevant descriptions of the present disclosure, which willnot be repeated herein.

FIG. 5 is a flowchart illustrating an exemplary motion monitoring methodaccording to some embodiments of the present disclosure. As shown inFIG. 5 , process 500 may include the following steps.

In step 510, obtaining a movement signal of a user during motion.

In some embodiments, the step 510 may be performed by the obtainingmodule 210. The movement signal refers to human body parameterinformation of the user during motion. In some embodiments, the humanbody parameter information may include, but is not limited to, one ormore of an electromyographic signal, an attitude signal, anelectro-cardio signal, a temperature signal, a humidity signal, a bloodoxygen concentration, and a respiration rate, etc. In some embodiments,an electromyographic sensor in the obtaining module 210 may collect theelectromyographic signal of the user during motion. For example, whenthe user performs a seated chest press, the electromyographic sensors ina wearable device corresponding to human pectoral muscles, latissimusdorsi, etc. may obtain the electromyographic signals of correspondingmuscle positions of the user. For another example, when a user performsa deep squat, the electromyographic sensors in the wearable devicecorresponding to gluteus maximus and quadriceps can collect theelectromyographic signals of the corresponding muscle positions. Foranother example, when the user is running, the electromyographic sensorsin the wearable device corresponding to a gastrocnemius muscle and otherpositions can obtain the electromyographic signals of the correspondingmuscle positions. In some embodiments, the attitude sensor in theobtaining module 210 may obtain an attitude signal of the user duringmotion. For example, when the user performs a barbell bench press, theattitude sensor in the wearable device corresponding to the humantriceps, etc., can obtain the attitude signal of the triceps, etc. Forexample, when the user performs a dumbbell flyover, the attitude sensorset at a position such as a human deltoid muscle may obtain the attitudesignal of the corresponding position. In some embodiments, a pluralityof attitude sensors may obtain attitude signals of a plurality ofportions of the user during motion, and the attitude signals of aplurality of portions may reflect a relative movement between differentparts of the body. For example, an attitude signal at an arm and anattitude signal at a torso can reflect a movement condition of the armrelative to the torso. In some embodiments, the attitude signal isassociated with a type of the attitude sensor. For example, when theattitude sensor is an angular velocity tri-axis sensor, an obtainedattitude signal is angular velocity information. For another example,when the attitude sensor is the angular velocity tri-axis sensor and anacceleration tri-axis sensor, the obtained attitude signal is theangular velocity information and acceleration information. For example,when the attitude sensor is a strain gauge sensor, the strain gaugesensor can be arranged at a user's joint position, by measuring theresistance in the strain gauge sensor that varies with the stretchlength, the obtained attitude signal may be displacement information,stress, etc., and a bending angle and a bending direction at the user'sjoint may be represented through these attitude signals. It is importantto note that the parameter information configured to reflect therelative motion of the user's body may be feature informationcorresponding to the attitude signal, which can be obtained by usingdifferent types of attitude sensors according to the type of the featureinformation.

In some embodiments, the movement signal may include theelectromyographic signal and an attitude signal of a particular part ofthe user's body. The electromyographic signal and the attitude signalcan reflect a movement state of the particular part of the user's bodyfrom different angles. In simple terms, the attitude signal of aspecific part of the user's body can reflect the type of movement,movement amplitude, movement frequency, etc. of the specific part. Theelectromyographic signal may reflect a muscle state of the particularpart during motion. In some embodiments, by measuring theelectromyographic signal and/or the attitude signal of the same bodypart, whether the movement of that part is standard can be betterassessed.

In step 520, monitoring the movement of the user during motion based atleast on feature information corresponding to the electromyographicsignal or feature information corresponding to the attitude signal.

In some embodiments, the step 520 may be performed by the processingmodule 220 and/or the processing device 110. In some embodiments, thefeature information corresponding to the electromyographic signal mayinclude, but is not limited to, one or more of frequency information,amplitude information, etc. The feature information corresponding to theattitude signal is parameter information configured to represent therelative motion of the user's body. In some embodiments, the featureinformation corresponding to the attitude signal may include, but is notlimited to, one or more of an angular velocity direction, an angularvelocity value, an acceleration value of angular velocity, etc. In someembodiments, the feature information corresponding to the attitudesignal may further include an angle, displacement information (e.g., astretch length in a strain gauge sensor), stress, etc. For example, whenthe attitude sensor is a strain gauge sensor, the strain gauge sensormay be set at the user's joint position, and by measuring the resistancein the strain gauge sensor that varies with the stretch length, theobtained attitude signal may be displacement information, stress, etc.,which may represent the bending angle and the bending direction at theuser's joint. In some embodiments, the processing module 220 and/or theprocessing device 110 may extract the feature information correspondingto the electromyographic signal (e.g., frequency information, amplitudeinformation) or the feature information corresponding to the attitudesignal (e.g., the angular velocity direction, the angular velocityvalue, the acceleration value of angular velocity, the angle, thedisplacement information, the stress, etc.), and monitor the movement ofthe user during motion based on the feature information corresponding tothe electromyographic signal or the feature information corresponding tothe attitude signal. The monitoring of the movement during motionincludes user's movement-related information. In some embodiments,movement-related information may include one or more of a movement type,number of movements, a movement quality (e.g., whether the movementmeets a standard), a movement time, etc. The movement type is a fitnessmovement performed by the user during motion. In some embodiments, themovement type may include, but is not limited to, one or more of seatedchest presses, deep squats, hard pulls, plank supports, running,swimming, etc. The number of movements refers to the number of times theuser performs the movement during motion. For example, if the userperforms 10 seated chest clamps during motion, 10 is the number ofmovements. The movement quality refers to the standard degree of thefitness movement performed by the user related to a standard fitnessmovement. For example, when the user performs a deep squat movement, theprocessing device 110 may determine a movement type of the user based onthe feature information corresponding to the movement signal (theelectromyographic signal and the attitude signal) of a particularspecific muscle location (gluteus maximus, quadriceps, etc.) anddetermine the movement quality of the user during performing the deepsquat movement based on the movement signal. The movement time is thetime corresponding to one or more movement types of the user or thetotal time of the movement process. Detailed descriptions of monitoringthe movement of the user during motion based on the feature informationcorresponding to the electromyographic signal and/or the featureinformation corresponding to the attitude signal may be found in FIG. 6and its relevant descriptions of the present disclosure.

In some embodiments, the processing device 110 may use one or moremovement recognition models to recognize and monitor the movement of theuser during motion. For example, the processing device 110 may input thefeature information corresponding to the electromyographic signal and/orthe feature information corresponding to the attitude signal into amovement recognition model, and the movement recognition model outputsinformation related to the user's movement. In some embodiments, themovement recognition model may include different types of movementrecognition models, for example, a model configured to recognize themovement type of the user, or a model configured to identify movementquality of the user, etc.

It should be noted that the above description regarding process 500 isfor exemplary and illustrative purpose only, and does not limit thescope of application of the present disclosure. For those skilled in theart, various amendments and changes can be made to the process 500 underthe guidance of the present disclosure. However, these amendments andchanges remain within the scope of the present disclosure. For example,extraction of the feature information corresponding to theelectromyographic signal or the feature information corresponding to theattitude signal in step 520 may be performed through the processingdevice 110, or in some embodiments, by processing module 220. Forexample, the user's movement signal is not limited to the aboveelectromyographic signal, attitude signal, electro-cardio signal,temperature signal, humidity signal, blood oxygen concentration,respiration rate, but may also include other human physiologicalparameter signal, and the physiological parameter signals involved inhuman movement can be all considered as the movement signal in theembodiments of the present disclosure.

FIG. 6 is a flowchart of an exemplary process for monitoring a movementof a user during motion according to some embodiments of the presentdisclosure. As shown in FIG. 6 , process 600 may include the followingsteps.

In step 610, segmenting, based on the feature information correspondingto the electromyographic signal or the feature information correspondingto the attitude signal, the movement signal.

In some embodiments, the step may be performed by the processing module220 and/or the processing device 110. The process of obtaining themovement signal (e.g., the electromyographic signal, the attitudesignal) of the user during motion is continuous, and a movement of theuser during motion may be a combination of a plurality of sets ofmovement or a combination of different movement types. To analyze eachmovement of the user during motion, the processing module 220 maysegment the movement signal of the user based on the feature informationcorresponding to the electromyographic signal or the feature informationcorresponding to the attitude signal. The segmenting the movement signalof the user herein refers to dividing the movement signal into signalsegments having same or different durations, or extract one or moresignal segments having a specific duration from the movement signal. Insome embodiments, each segment of the movement signal may correspond toone or more complete movement of the user. For example, when a userperforms a deep squat, the user's movement from a standing position to asquat position and then getting up to return to the standing positioncan be considered as completing the deep squat, and the movement signalcollected by the obtaining module 210 during this process can beconsidered as one segment (or one cycle) of the movement signal, afterwhich the movement signal collected by the obtaining module 210 from thenext deep squat completed by the user can be considered as anothersegment of the movement signal. In some embodiments, each movementsignal may also correspond to a portion of the user's movement, where aportion of the movement may be understood as a portion of a completemovement. For example, when a user performs a deep squat, the user'smovement from a standing position to a squat position may be consideredas one segment of the movement, and getting up to return to the standingposition may be considered as another segment of the movement. A changein each movement of the user during motion may cause theelectromyographic signal and the attitude signal of a corresponding bodypart to change. For example, when the user performs a squat, theelectromyographic signal and the attitude signal of the muscles in thecorresponding parts of the user's body (e.g., arms, legs, hips, abdomen)fluctuate less when the user stands; when the user squats from thestanding position, the electromyographic signal and the attitude signalof the muscles in the corresponding parts of the user's body fluctuatemore, e.g., amplitude information corresponding to signals of differentfrequencies of the electromyographic signal becomes greater, or anangular velocity value, a direction of angular velocity, an accelerationvalue of angular velocity, an angle, displacement information, stress,etc. of the attitude signal may also change. When the user gets up froma squatting state to a standing state, the amplitude informationcorresponding to the electromyographic signal and the angular velocityvalue, the direction of angular velocity, the acceleration value ofangular velocity, the angle, the displacement information, and thestress corresponding to the attitude signal may change again. Based onthis situation, the processing module 220 may segment, based on thefeature information corresponding to the electromyographic signal or thefeature information corresponding to the attitude signal, the movementsignal. Detailed descriptions of segmenting the movement signal based onthe feature information corresponding to the electromyographic signal orthe feature information corresponding to the attitude signal may befound in FIG. 7 and FIG. 8 of the present disclosure and their relateddescriptions.

In step 620, monitoring, based on at least one segment of the movementsignal, the movement of the user during motion.

The step may be performed by processing module 220 and/or processingdevice 110. In some embodiments, monitoring of the movement of the userbased on at least one segment of the movement signal may includematching the at least one segment of the movement signal with at leastone segment of a preset movement signal to determine the movement typeof the user. The at least one segment of the preset movement signal isstandard movement signals corresponding to different movements that arepreset in a database. In some embodiments, a movement type of the userduring motion may be determined by determining a matching degree of theat least one segment of the movement signal and the at least one segmentof the preset movement signal. Further, the movement type of the usermay be determined by determining whether the matching degree of themovement signal and the preset movement signal is within a firstmatching threshold range (e.g., greater than 80%). If so, the movementtype of the user during motion is determined based on the movement typecorresponding to the preset movement signal. In some embodiments,monitoring the movement of the user during motion based on the at leastone segment of the movement signal may further include determining themovement type of the user during motion by matching the featureinformation corresponding to the at least one segment of theelectromyographic signal and feature information corresponding to anelectromyographic signal of the at least one segment of the presetmovement signal. For example, match degree(s) between one or morefeature information (e.g., frequency information, amplitude information)of the segment of the electromyographic signal and the one or morefeature information of the segment of the preset movement signal may becalculated respectively, and a determination is made as to whether aweighted matching degree of the one or more feature information or anaverage matching degree of the one or more feature information is withina first matching threshold. If so, the movement type of the user duringmotion is determined based on the movement type corresponding to thepreset movement signal. In some embodiments, monitoring the movement ofthe user during motion based on the at least one segment of the movementsignal may further include determining the movement type of the userduring motion by matching the feature information corresponding to theat least one segment of the attitude signal with the feature informationcorresponding to the attitude signal of the at least one segment of thepreset movement signal. For example, the matching degree of the one ormore feature information (e.g., the angular velocity value, the angularvelocity direction and the acceleration value of the angular velocity,the angle, the displacement information, the stress, etc.) of onesegment of the attitude signal and the one or more feature informationof one segment of the preset movement signal are calculated respectivelyto determine whether the weighted matching degree or the averagematching degree of the one or more feature information is within thefirst matching threshold. If so, the movement type of the user isdetermined according to preset a movement type corresponding to thepreset movement signal. In some embodiments, monitoring the movement ofthe user during motion based on the at least one segment of the movementsignal may further include determining the movement type of the userduring motion by matching the feature information corresponding to theelectromyographic signal and the feature information corresponding tothe attitude signal of the at least one segment of the movement signaland the feature information corresponding to the electromyographicsignal and the feature information corresponding to the attitude signalof the at least one segment of the preset movement signal.

In some embodiments, monitoring the movement of the user during motionbased on the at least one segment of the movement signal may includedetermining the movement quality of the user by matching the at leastone segment of the movement signal with the at least one segment of thepreset movement signal. Further, if a matching degree of the movementsignal and the preset movement signal is within a second matchingthreshold range (e.g., greater than 90%), the movement quality of theuser during motion meets the standard. In some embodiments, determiningthe movement of the user during motion based on the movement signal ofthe at least one segment may include determining the movement quality ofthe user during motion by matching the one or more feature informationof the movement signal of the at least one segment with the one or morefeature information of the at least one segment of the preset movementsignal. It should be noted that a segment of the movement signal may bea movement signal of a complete movement or a movement signal of apartial of a complete movement. In some embodiments, for a complexcomplete movement, there may be different ways of force generation atdifferent stages of the complete movement, that is, there may bedifferent movement signals at the different stages of the movement, andthe user movement can be monitored in real time, and thus, the accuracyof the monitored movement signal at the different stages of the completemovement can be improved.

It should be noted that the above description of the process 600 is forexample and illustration purposes only and does not limit the scope ofapplication of the present disclosure. For those skilled in the art,various amendments and changes can be made to process 600 under theguidance of the present disclosure. However, these amendments andchanges remain within the scope of the present disclosure. For example,in some embodiments, the user's movement may also be determined througha movement recognition model or a manually preset model.

FIG. 7 is a flowchart of an exemplary process for segmenting a movementsignal according to some embodiments of the present disclosure. As shownin FIG. 7 , process 700 may include the following steps.

In step 710, determining, based on a time domain window of theelectromyographic signal or the attitude signal, at least one targetfeature point from within the time domain window according to a presetcondition.

In some embodiments, the step may be performed by the processing module220 and/or the processing device 110. The time domain window of theelectromyographic signal includes an electromyographic signal over arange of time, and the time domain window of the attitude signalincludes an attitude signal over a same range of time. A target featurepoint refers to a signal of the movement signal with a target feature,which may represent a stage of the user's movement. For example, when auser performs a seated chest press, at the beginning, the user's armsare extended outward horizontally, begin to rotate internally, cometogether, and finally return to the extended state again in thehorizontal direction, this process is a complete seated chest pressmovement. When the user performs a seated chest press movement, thefeature information corresponding to the electromyographic signal or theattitude signal is different in each stage. By analyzing the featureinformation corresponding to the electromyographic signal (e.g.,amplitude information, frequency information) or the feature informationcorresponding to the attitude signal (e.g., the angular velocity value,the direction of angular velocity, the acceleration value of angularvelocity, the angle, the displacement information, the stress, etc.),the target feature point corresponding to a stage of the user's movementmay be determined. In some embodiments, one or more target featurepoints may be determined from the time domain window based on the presetcondition. In some embodiments, the preset condition may include one ormore of a change in the direction of the angular velocity correspondingto the attitude signal, the angular velocity corresponding to theattitude signal being greater than or equal to an angular velocitythreshold, the angle corresponding to the attitude signal reaching anangular threshold, the change of the angular velocity valuecorresponding to the attitude signal being the extreme value, and theamplitude information corresponding to the electromyographic signalbeing greater than or equal to an electromyographic threshold. In someembodiments, the target feature points at the different stages of amovement may correspond to different preset conditions. For example, inthe seated chest press, a preset condition for a target feature pointwhen the user's arms are horizontally extended outward and then start tointernally rotate is different from a preset condition for a targetfeature point when the arms are brought together. In some embodiments,the target feature points of different movements may correspond todifferent preset conditions. For example, the chest press movement andbent-over movement are different, and the preset conditions regardingthe respective preset target feature points in these two movements arealso different. Exemplary descriptions of the preset condition may referto the description of a movement start point, a movement middle pointand a movement end point in the present disclosure.

In other embodiments, the at least one target feature point may bedetermined, based on the both time domain windows of theelectromyographic signal and the attitude signal, from the time domainwindows according to the preset condition. The time domain windows ofthe electromyographic signal and the attitude signal both include theelectromyographic signal and the attitude signal over a range of time.The time of the electromyographic signal corresponds to the time of theattitude signal. For example, a time point of the electromyographicsignal when the user starts to move is the same as a time point of theattitude signal when the user starts to move. The target feature pointhere may be determined by combining the feature informationcorresponding to the electromyographic signal (e.g., the amplitudeinformation) and the feature information corresponding to the attitudesignal (e.g., the angular velocity value, the direction of angularvelocity, the acceleration value of angular velocity, the angle, etc.).

In step 720, segmenting, based on the at least one target feature point,the movement signal.

In some embodiments, the step 720 may be performed by the processingmodule 220 and/or the processing device 110. In some embodiments, thetarget feature point in the electromyographic signal or the attitudesignal may be one or more, and the movement signal may be divided intomultiple segments by one or more target feature points. For example,when there is a target feature point in the electromyographic signal,the target feature point may divide the electromyographic signal intotwo segments, where the two segments may include the electromyographicsignal before the target feature point and the electromyographic signalafter the target feature point. Alternatively, the processing module 220and/or the processing device 110 may extract the electromyographicsignal for a certain time range around the target feature point as asegment of the electromyographic signal. For another example, when theelectromyographic signal has a plurality of target feature points (e.g.,n-target feature points, and the first target feature point is not abeginning of the time domain window and the n^(th) target feature pointis not an end of the time domain window), the electromyographic signalmay be divided into (n+1) segments based on the n target feature points.For another example, when the electromyographic signal has the pluralityof target feature points (e.g., n-target feature points, and the firsttarget feature point is the beginning of the time domain window and then^(th) target feature point is not the end of the time domain window),the electromyographic signal may be divided into n segments based on then target feature points. As a further example, when theelectromyographic signal has the plurality of target feature points(e.g., n-target feature points, and the first target feature point isthe beginning of the time domain window and the n^(th) target featurepoint is the end of the time domain window), the electromyographicsignal may be divided into (n−1) segments based on the n target featurepoints. It should be noted that the movement stage corresponding to thetarget feature point may include one or more types, and a plurality ofmovement stages corresponding to the target feature point may be used asa benchmark for segmenting the movement signal. For example, themovement stage corresponding to the target feature point may include themovement start point and the movement end point, with the movement startpoint preceding the movement end point, and the movement signal herebetween the movement start point and a next movement start point may beconsidered as a segment of the movement signal.

In some embodiments, the target feature point may include one or more ofthe movement start point, the movement middle point, or the movement endpoint.

To describe the segmentation of the movement signal, take the targetfeature point including all of the movement start point, the movementmiddle point and the movement end point as an exemplary illustration.The movement start point may be considered as a start point of a usermovement cycle. In some embodiments, different movements may correspondto the different preset conditions. For example, in the seated chestpress, the preset condition may be that the direction of the angularvelocity of the movement after the movement start point changes relativeto the direction of the angular velocity of the movement before themovement start point, or that the value of the angular velocity at themovement start point is approximately 0 and the acceleration value ofthe angular velocity at the movement start point is greater than 0. Inother words, when the user performs the seated chest press, the movementstarting point may be set to the point when the arms are extendedoutward horizontally and start to internally rotate. For anotherexample, in a bent-over movement, the preset condition may be that theangle of arm lift is greater than or equal to an angle threshold.Specifically, when the user performs a bent-over movement, the angle ofarm lift when the user's arm is horizontal is 0°, the angle of arm liftwhen the arm is down is negative, and the angle of arm lift when the armis up is positive. When the user's arm is raised from the horizontalposition, the arm is raised at an angle greater than 0. The point intime when the angle of the arm lift reaches the angle threshold may beconsidered as the movement start point. The angle threshold may be −70°to −20°, or as a preference, the angle threshold may be −50° to −25°. Insome embodiments, to further ensure the accuracy of a selected movementstart point, the preset condition may also include that the angularvelocity of the arm within a specific range of time after the movementstart point may be greater than or equal to an angular velocitythreshold. The angular velocity threshold may range from 5°/s˜50°/s.According to preference of example, the angular velocity threshold mayrange from 10°/s˜30°/s. For example, when a user performs a bent-overmovement, the angular velocity of the arm is continuously greater thanthe angular velocity threshold for a specific time range (e.g., 0.05 s,0.1 s, 0.5 s) after an angular threshold is reached and the user's armis continuously raised upward. In some embodiments, if the angularvelocity of the selected movement start point according to the presetcondition is less than the angular velocity threshold within a specificrange of time, the preset condition continues until a movement startpoint is determined.

In some embodiments, the movement middle point may be a point within onemovement cycle from the start point. For example, when the user performsthe seated chest press, a start point of the movement may be set to thetime when the arms extend outward horizontally and begin to internallyrotate, and the time when the arms come together may be used as amovement middle point of the user. In some embodiments, the presetcondition may be that a direction of the angular velocity at the pointin time after the movement middle point changes relative to a directionof the angular velocity at the point in time before the movement middlepoint, and an angular velocity value at the movement middle point isapproximately zero, wherein the direction of the angular velocity at themovement middle point is opposite to the direction of the angularvelocity at the movement start point. In some embodiments, to improvethe accuracy of the selection of the movement middle point, a change ofthe angular velocity (acceleration of angular velocity) in a firstspecific time range after the movement middle point (e.g., 0.05 s, 0.1s, 0.5 s) may be greater than an acceleration threshold of angularvelocity (e.g., 0.05 rad/s). In some implementations, the amplitudeinformation in the electromyographic signal corresponding to themovement middle point is greater than the electromyographic thresholdwhile the movement middle point satisfies the preset condition describedabove. Since the different movements correspond to differentelectromyographic signals, the electromyographic threshold is related tothe user movement and the target electromyographic signal. In the seatedchest press, the electromyographic signal at the pectoral muscle is thetarget electromyographic signal. In some embodiments, the positioncorresponding to the middle point of the movement (also may be called as“middle position”) may be approximated as a maximum point of muscleforce, where the electromyographic signal may have a relatively greatvalue. It should be noted that the electromyographic signal at the partof the user's body when the user performs the movement during motion issubstantially higher than the electromyographic signal at the part ofthe user's body when the user does not perform the movement duringmotion (when the muscle in the particular part may be considered as aresting state). For example, an amplitude of the electromyographicsignal at the part of the user's body when the user's movement reachesthe middle position is 10 times higher than that in the resting state.In addition, the relationship between the amplitude of theelectromyographic signal at the part of the user when the movementposition reaches the middle position (the movement middle point) and theamplitude of the electromyographic signal in the resting state may bedifferent according to the different movement types performed by theuser, and the relationship between the two may be adapted according tothe actual movement. In some embodiments, to improve the accuracy of theselection of the movement middle point, the amplitude corresponding to asecond specific time range after the movement middle point (e.g., 0.05s, 0.1 s, 0.5 s) may be continuously greater than the electromyographicthreshold. In some embodiments, in addition to the above presetcondition (e.g., the angular velocity and an amplitude condition of theelectromyographic signal), a Euler angle (also referred to as angle) ofthe movement middle point and the start position satisfies a certaincondition preset to determine the movement middle point. For example, inthe seated chest press, the Euler angle at the movement middle pointrelative to the movement start point may be greater than one or moreEuler angle thresholds (also known as angle thresholds). For example,with a front-to-back direction of the human body as an X-axis, aleft-right direction of the human body as a Y-axis, and a heightdirection of the human body as a Z-axis, an Euler angle changed in the Xand Y directions may be less than 25°, and the Euler angle changed inthe Z direction may be greater than 40° (the movement of the seatedchest press is mainly related to the rotation at the Z-axis direction,the above parameters are only reference examples). In some embodiments,the electromyographic thresholds and/or the Euler angle thresholds maybe stored in advance in the memory or hard drive of the wearable device130, or in the processing device 110, or calculated based on an actualcondition and adjusted in real time.

In some embodiments, the processing module 220 may determine, based onthe time domain window of the electromyographic signal or the attitudesignal, the movement middle point from a time domain window at a timepoint after the movement start point according to a preset condition. Insome implementations, after the movement middle point is determined,whether there are other time points that meet the preset conditionwithin the time range from the movement start point to the movementmiddle point may be re-verified, and if so, a movement start pointclosest to the movement middle point may be selected as the bestmovement start point. In some embodiments, if the difference between thetime of the movement middle point and the time of the movement startpoint is greater than a specific time threshold (e.g., ½ or ⅔ of amovement cycle), the movement middle point is invalid, and the movementstart point and movement middle point are re-determined based on presetcondition.

In some embodiments, the movement end point may be a time point that iswithin one movement cycle from the movement start point and after themovement middle point. For example, the movement end point may be set toas a point that is one movement cycle from the movement start point, andthe movement end point herein may be considered the end of a movementcycle of the user. For example, when the user performs the seated chestpress, the movement start point may be set as a time point when the armsextend horizontally to the left and right and start internal rotation,the time point when the arms close together may be the movement middlepoint of the user, and the time point when the arms return to theextended state again from the horizontal direction may correspond to themovement end point of the user. In some embodiments, the presetcondition may be that a changed angular velocity value corresponding tothe attitude signal is an extreme value. In some embodiments, to preventjitter misjudgment, the change in Euler angle should exceed a certainEuler angle threshold, e.g., 20°, in the time range from the movementmiddle point to the movement end point. In some embodiments, theprocessing module 220 may determine the movement end point from the timedomain window after the movement middle point based on the time domainwindows of the electromyographic signal and the attitude signalaccording to the preset condition. In some embodiments, if thedifference between the time of the movement end point and the time ofthe movement middle point is greater than a specific time threshold(e.g., ½ of a movement cycle), the movement start point and the movementmiddle point are invalid, and the movement start point, movement middlepoint, and movement end point are re-determined based on the presetcondition.

In some embodiments, at least one set of the movement start point, themovement middle point, and the movement end point in the movement signalmay be repeatedly determined, and the movement signal may be segmentedbased on the at least one set of the movement start point, the movementmiddle point, and the movement end point as the target feature points.The step may be performed by the processing module 220 and/or theprocessing device 110. It should be noted that segmentation of themovement signal is not limited to be based on the above movement startpoint, movement middle point and movement end point, but may alsoinclude other time points. For example, for the seated chest press, 5time points may be selected according to the above steps, a first timepoint may be a movement start point, a second time point may be a momentof the maximum angular velocity of the internal rotation, a third timepoint may be the movement middle point, a fourth time point may be themoment of the maximum angular velocity of external rotation, a fifthtime point may be the moment when the arms return to extend left andright, and the angular velocity is 0, that is, the movement end point.In this example, compared to the movement start point, movement middlepoint and movement end point in the above steps, the second time pointis added as a ¼ marker point of the movement cycle, the movement endpoint described in the above embodiments is used as the fourth timepoint for marking the ¾ position of the movement cycle, and the fifthtime point is added as an end point of the complete movement. For theseated chest press, more time points are used here, and a recognition ofthe movement quality may be done based on the signal of the first ¾ ofthe movement cycle (i.e., the recognition of the movement quality for asingle cycle does not depend on a complete analysis of the signal of awhole cycle), which can complete the monitoring and feedback of theuser's movement without the end of a current cycle. At same time, allsignals of the process of the whole movement may be completely recordedto be easily uploaded to the cloud or the mobile terminal device, thusmore methods may be adopted to monitor the user's movement. For morecomplex movement, the cycle of the movement may be quite long, and thestages for the movement have different force pattern. In someembodiments, the above method of determining each time point may beadopted to divide the movement into multiple stages, and the signal foreach stage may be recognized and fed back separately to improvetimeliness of feedback of the user's movement.

It should be noted that the above segmentation and monitoring of themovement signal based on the movement start point, movement middle pointand movement end point as a set of target feature point is only anexemplary illustration. In some embodiments, the user's movement signalmay also be segmented and monitored based on any one or more of themovement start point, the movement middle point and the movement endpoint as the target feature point. For example, the movement signal maybe segmented and monitored by using the movement start point as thetarget feature point. For another example, the movement start point andthe movement end point may be used as a set of target feature points tosegment and monitor the movement signal, and other time point or timeranges that can be used as the target feature point are within the scopeof protection of the present disclosure.

It should be noted that the above description of the process 700 is forexample and illustration purposes only and does not limit the scope ofapplication of the present disclosure. For those skilled in the art,various amendments and changes can be made to the process 700 under theguidance of the present disclosure. However, these amendments andchanges remain within the scope of the present disclosure. For example,step 710 and step 720 may be performed simultaneously in the processingmodule 220. For another example, step 710 and step 720 may be performedsimultaneously in the processing module 220 and the processing device110, respectively.

FIG. 8 is a diagram illustrating exemplary movement signal segmentationaccording to some embodiments of the present disclosure. A horizontalcoordinate in FIG. 8 may indicate a motion time of a user, and avertical coordinate may indicate amplitude information of anelectromyographic signal of a muscle part (e.g., pectoralis major)during seated chest press. Also included in FIG. 8 are an angularvelocity curve and a Euler angle curve corresponding to an attitudesignal of the wrist position of the user during motion. The angularvelocity curve is configured to represent a velocity change of the userduring motion and the Euler angle curve is configured to represent aposition situation of a user's body part during motion. As shown in FIG.8 , point A1 is determined as the movement start point according to thepreset condition. Specifically, a direction of the angular velocity at atime point after the user's movement start point A1 changes relative tothe direction of the angular velocity at a time point before themovement start point A1. Further, the angular velocity value at themovement start point A1 is approximately 0, and an acceleration value ofthe angular velocity at the movement start point A1 is greater than 0.

Refer to FIG. 8 , point B1 is determined as the movement middle pointaccording to the preset condition. Specifically, the direction of theangular velocity at the time point after the user's movement middlepoint B1 changes relative to the direction of the angular velocity atthe time point before the movement middle point B1, and the angularvelocity value at the movement middle point B1 is approximately 0. Thedirection of the angular velocity at the movement middle point B1 isopposite to the direction of the angular velocity at the movement startpoint A1. In addition, the amplitude of the electromyographic signal(shown as the “electromyographic signal” in FIG. 8 ) corresponding tothe movement middle point B1 is greater than the electromyographicthreshold.

Continue to refer to FIG. 8 , point C1 is determined as the movement endpoint according to the preset condition. Specifically, a changed angularvelocity value at the movement end point C1 is the extreme value fromthe movement start point A1 to the movement end point C1. In someembodiments, the process 700 may complete the movement segmentationshown in FIG. 8 , such that the movement signal from the movement startpoint A1 to the movement end point C1 shown in FIG. 8 may be consideredas a segment of the motion.

It is noted that in some embodiments, if a time interval between themovement middle point and the movement start point is greater than aspecific time threshold (e.g., ½ of a movement cycle), the processingmodule 220 may re-determine the movement start point to improve theaccuracy of the movement segmentation. The specific time threshold heremay be stored in the memory or the hard drive of the wearable device130, or in the processing device 110, or calculated or adjusted based onthe actual situation of the user during motion. For example, if the timeinterval between the movement start point A1 and the movement middlepoint B1 in FIG. 8 is greater than a specific time threshold, theprocessing module 220 may re-determine the movement start point, therebyimproving the accuracy of the movement segmentation. In addition, thesegmentation of the movement signal is not limited to be based on theabove movement start point A1, movement middle point B1 and movement endpoint C1, but may also include other time points, and selection of thetime points may be made according to complexity of the movement.

When obtaining the user's movement signal, other physiological parameterinformation of the user (e.g., a heart rate signal), external conditionsuch as a relative movement of the obtaining module 210 and the humanbody during motion or compression of the obtaining module 210 may affectthe quality of the movement signal, for example, resulting in an abruptchange in the electromyographic signal, thereby affecting the monitoringof the movement. For ease of description, an abrupt electromyographicsignal may be described by using a singularity, and an exemplarysingularity may include a burr signal, a discontinuous signal, etc. Insome embodiments, monitoring the movement of the user during motionbased at least on the feature information corresponding to theelectromyographic signal or the feature information corresponding to theattitude signal may further include: pre-processing theelectromyographic signal in a frequency domain or a time domain,obtaining, based on the pre-processed electromyographic signal, thefeature information corresponding to the electromyographic signal, andmonitoring, based on the feature information corresponding to theelectromyographic signal or the feature information corresponding to theattitude signal, the movement of the user during motion. In someembodiments, pre-processing the electromyographic signal in thefrequency domain or the time domain may include filtering theelectromyographic signal in the frequency domain to select or retaincomponents of the electromyographic signal in a particular frequencyrange in the frequency domain. In some embodiments, the obtaining module210 may obtain an electromyographic signal in a frequency range of 1Hz-1000 Hz, filter the electromyographic signal and select anelectromyographic signal in a specific frequency range (e.g., 30 Hz-150Hz) for subsequent processing. In some embodiments, the specificfrequency range may be 10 Hz-500 Hz. According to preference of example,the specific frequency range may be 15 Hz-300 Hz or 30 Hz-150 Hz. Insome embodiments, a filtering process may include a low-pass filterprocessing. In some embodiments, the low-pass filter may include an LCpassive filter, an RC passive filter, an RC active filter, a passivefilter composed of special elements. In some embodiments, the passivefilter composed of the special elements may include one or more of apiezoelectric ceramic filter, a crystal filter, and an acoustic surfacefilter. It should be noted that the specific frequency range is notlimited to the above range, but may also be other ranges, which may beselected according to the actual situation. More descriptions ofmonitoring, according to the feature information corresponding to theelectromyographic signal or the feature information corresponding to theattitude signal, the movement of the user during motion may be found inFIG. 5 , FIG. 6 of the present disclosure and their relevantdescriptions.

In some embodiments, pre-processing the electromyographic signal in thefrequency domain or the time domain may further include signalcorrection processing of the electromyographic signal in the timedomain. The signal correction processing refers to a correction to thesingularity (e.g., the burr signal, the discontinuous signal, etc.) inthe electromyographic signal. In some embodiments, the signal correctionprocessing of the electromyographic signal in the time domain mayinclude determining the singularity in the electromyographic signal,i.e., determining the abrupt signal in the electromyographic signal. Thesingularity may be a sudden change in the amplitude of anelectromyographic signal within a certain moment, causing adiscontinuity in the signal. For another example, the electromyographicsignal is morphologically smooth and there is no abrupt change in theamplitude of the electromyographic signal, but there is the abruptchange in the first-order differential of the electromyographic signal,and the first-order differential is discontinuous. In some embodiments,the method of determining the singularity in the electromyographicsignal may include, but is not limited to, one or more of Fouriertransform, wavelet transform, fractal dimension, etc. In someembodiments, the signal correction processing of the electromyographicsignal in the time domain may include removing the singularity in theelectromyographic signal, for example, removing signals within a periodof time at and near the singularity. Alternatively, the signalcorrection processing of the electromyographic signal in the time domainmay include correcting the singularity of the electromyographic signalaccording to the feature information of the electromyographic signal inthe specific time range, such as adjusting the amplitude of thesingularity based on the signals around the singularity. In someembodiments, the feature information of the electromyographic signal mayinclude the amplitude information, the statistic information of theamplitude information, etc. The statistic information of amplitudeinformation (also known as amplitude entropy) refers to a distributionof the amplitude information of the electromyographic signal in the timedomain. In some embodiments, after a location (e.g., the time point) ofthe singularity in the electromyographic signal is determined through asignal processing algorithm (e.g., the Fourier transform, the wavelettransform, the fractal dimension), the singularity may be correctedbased on the electromyographic signal in the specific time range beforeor after the location of the singularity. For example, when thesingularity is an abrupt trough, the electromyographic signal at theabrupt trough can be supplemented based on the feature information(e.g., the amplitude information, the statistic information of theamplitude information) of the electromyographic signal in a specifictime range (e.g., 5 ms-60 ms) before or after the abrupt trough.

Exemplary illustration with the singularity as the burr signal, FIG. 9is a flowchart of an exemplary process for pre-processing anelectromyographic signal according to some embodiments of the presentdisclosure. As shown in FIG. 9 , the process 900 may include:

Step 910, selecting, based on the time domain window of theelectromyographic signal, different time windows from the time domainwindow of the electromyographic signal, wherein the different timewindows respectively cover different time ranges.

In some embodiments, the step may be performed by the processing module220 and/or the processing device 110. In some embodiments, the differentwindows may include at least one specific window. A specific window is awindow with a specific time length selected from the time domain window.For example, a time length of the specific window may be 100 ms when thetime length of the time domain window of the electromyographic signal is3 s. In some embodiments, a specific window may include a plurality ofdifferent time windows. Merely as way of exemplary illustration, thespecific window may include a first time window and a second timewindow, and the first time window may refer to a window corresponding toa partial time length of the specific window, for example, when the timelength of the specific window is 100 ms, the time length of the firsttime window may be 80 ms. The second time window may be another windowcorresponding to the partial time length of the specific window. Forexample, when the specific window is 100 ms, the second time window maybe 20 ms. In some embodiments, the first time window and the second timewindow may be consecutive time windows within a same specific window. Insome embodiments, the first time window and the second time window mayalso be two discrete or overlapping time windows within the samespecific window. For example, when the time length of the specificwindow is 100 ms, the time length of the first time window may be 80 msand the time length of the second time window may be 25 ms, in whichcase the second time window is overlapped with the first time window in5 ms. In some embodiments, the processing module 220 may slide andupdate the specific window sequentially from an initially time point ofthe time domain window of the electromyographic signal according to thespecific time length based on the time domain window of theelectromyographic signal, and may continue to divide an updated specificwindow into the first time window and the second time window. Thespecific time length mentioned here may be less than 1 s, 2 s, 3 s, etc.For example, the processing module 220 may select a specific window of aspecific time length of 100 ms and divide that specific window into afirst time window of 80 ms and a second time window of 20 ms. Further,the specific window may be updated by sliding along the time direction.A sliding distance here may be a time length of the second time window(e.g., 20 ms) or other suitable time lengths, e.g., 30 ms, 40 ms, etc.

Step 920, determining, based on the feature information corresponding tothe electromyographic signal in the different time windows, the burrsignal.

In some embodiments, the step may be performed by the processing module220 and/or the processing device 110. In some embodiments, the featureinformation corresponding to the electromyographic signal may include atleast one of the amplitude information, the statistic information of theamplitude information. In some embodiments, the processing module 220may obtain the amplitude information or the statistic information of theamplitude information corresponding to the electromyographic signal indifferent time windows (e.g., the first time window, the second timewindow) to determine the location of the burr signal. Detaileddescriptions of determining, based on the feature informationcorresponding to the electromyographic signal in different time windows,the location of the burr signal may be found in FIG. 10 and its relevantdescriptions.

It should be noted that the above description of the process 900 is forexample and illustration purposes only and does not limit the scope ofapplication of the present disclosure. For those skilled in the art,various amendments and changes can be made to process 900 under theguidance of the present disclosure. For example, the specific window isnot limited to include the first time window and the second time windowdescribed above, but may also include other time windows, for example, athird time window, a fourth time window, etc. In addition, the specificrange of moments before or after the position of the burr signal may beadapted according to the length of the burr signal, which will not befurther limited herein. However, these amendments and changes remainwithin the scope of the present disclosure.

FIG. 10 is a flow chart illustrating an exemplary process fordetermining a burr signal according to some embodiments of the presentdisclosure.

Step 1010, determining first amplitude information corresponding to theelectromyographic signal within the first time window and secondamplitude information corresponding to the electromyographic signalwithin the second time window.

In some embodiments, the step may be performed by the processing module220 and/or the processing device 110. In some embodiments, theprocessing module 220 may select the time length of the first timewindow and the second time window and extract the first amplitudeinformation corresponding to the electromyographic signal during thetime length of the first time window and the second amplitudeinformation corresponding to the electromyographic signal during thetime length of the second time window. In some embodiments, the firstamplitude information may include an average amplitude of theelectromyographic signal during the first time window, and the secondamplitude information may include the average amplitude of theelectromyographic signal during the second time window. For example, theprocessing module 220 may select a time length of a first time window as80 ms and extract the first amplitude information corresponding to theelectromyographic signal within the first time window, and theprocessing module 220 may select a time length of a second time windowas 20 ms and extract the second amplitude information corresponding tothe electromyographic signal within the second time window.

In some embodiments, a selection of the time length of the first timewindow and the time length of the second time window is related to theshortest burr signal length and amount of computation of the system. Insome embodiments, the time length of the first time window and the timelength of the second time window may be selected according to thefeature of the burr signal. The time length of an electro-cardio burrsignal is 40 ms-100 ms, the time interval between two burr signals inthe electro-cardio signal may be about 1 s, a peak point of the burrsignal is basically symmetrical on both sides, an amplitude distributionof the burr signal is relatively even on both sides, etc. In someembodiments, when the burr signal is the electro-cardio signal, a timelength less than the length of the burr signal, e.g., half the length ofthe burr signal, may be selected as the time length of the second timewindow, and the time length of the first time window may be greater than(e.g., four times) the time length of the second time window. In someembodiments, the time length of the first time window may be within arange of an interval (about 1 s) between burr signals minus the timelength of the second time window. It should also be noted that the aboveselected time length of the first time window and the time length of thesecond time window are not limited to the above description, as long asa sum of the time length of the second time window and the time lengthof the first time window is less than time intervals of adjacent twoburr signals, or the time length of the second time window is less thanthe single burr signal length, or an amplitude of the electromyographicsignal within the second time window and an amplitude of theelectromyographic signal the first time window may be discriminated.

Step 1020, judging whether a ratio of the second amplitude informationto the first amplitude information is greater than a threshold.

In some embodiments, the step may be performed by the processing module220 and/or the processing device 110. In some embodiments, theprocessing module 220 may determine whether the ratio of the secondamplitude information corresponding to the electromyographic signal inthe second time window to the first amplitude information correspondingto the electromyographic signal in the first time window is greater thanthe threshold. The threshold here may be stored in the memory or thehard drive of the wearable device 130, or in the processing device 110,or adjusted according to the actual situation. In some embodiments, thestep 1020 may proceed to step 1030 if the processing module 220 judgesthat the ratio of the second amplitude information to the firstamplitude information is greater than the threshold. In otherembodiments, if the processing module 220 determines that the ratio ofthe second amplitude information to the first amplitude information isnot greater than the threshold, step 1020 may proceed to step 1040.

Step 1030, performing a signal correction processing on theelectromyographic signal within the second time window.

In some embodiments, the step may be performed by the processing module220 and/or the processing device 110. In some embodiments, theprocessing module 220 may perform the signal correction processing onthe electromyographic signal within the second time window based on thecomparison result of the ratio of the second amplitude information tothe first amplitude information and the threshold in step 1020. Forexample, in some embodiments, if the ratio of the second amplitudeinformation to the first amplitude information is greater than thethreshold, then the electromyographic signal in the second time windowcorresponding to the second amplitude information is a burr signal. Insome embodiments, processing the electromyographic signal within thesecond time window may include performing signal correction processingon the electromyographic signal within the second time window based onthe electromyographic signal within a specific time range before orafter the second time window. In some embodiments, the signal correctionprocessing of the electromyographic signal within the second time windowmay include, but is not limited to, padding, interpolation, etc. In someembodiments, the specific time range herein may be 5 ms-60 ms. Accordingto preference of example, the specific time range may be 10 ms-50 ms or20 ms-40 ms. It should be noted that the specific time range is notlimited to the above range, for example, the specific time range may begreater than 60 ms, less than 5 ms, or other ranges. In practicalapplication scenarios, the specific time range may be adapted based onthe duration of the burr signal.

In step 1040, retaining an electromyographic signal within the secondtime window.

In some embodiments, the step may be performed by the processing module220 and/or the processing device 110. In some embodiments, theprocessing module 220 may perform retention on the electromyographicsignal within the second time window according to the comparison resultof the ratio of the second amplitude information to the first amplitudeinformation and the threshold in step 1020. For example, in someembodiments, the ratio of the second amplitude information to the firstamplitude information is not greater than the threshold, then theelectromyographic signal within the second time window corresponding tothe second amplitude information is a normal electromyographic signal,and the normal electromyographic signal may be retained, i.e., theelectromyographic signal within the second time window is retained.

It should be noted that the amplitude of the electromyographic signal isgradually increasing since electrical charges gradually accumulatesduring muscular exertion, so that the amplitude of the electromyographicsignal within two adjacent time windows (e.g., the first time window andthe second time window) does not change abruptly in the absence of aburr signal. In some embodiments, whether there is the burr signal inthe electromyographic signal may be determined and the burr signal maybe removed based on the process 1000 to realize a real-time processingof the burr signal, thereby enabling the wearable device 130 or themobile terminal device 140 to provide a real-time feedback of the motionstate to the user, and helping the user to perform motion morescientifically.

In some embodiments, the time length corresponding to the first timewindow may be greater than the time length corresponding to the secondtime window. In some embodiments, a specific time length correspondingto a specific window may be less than 1 s. In some embodiments, theratio of the time length corresponding to the first time window to thetime length corresponding to the second time window may be greater than2. In some embodiments, the time length corresponding to the first timewindow, the time length corresponding to the second time window, and thespecific time length corresponding to the specific window are selectedto ensure that the shortest burr signal (e.g., 40 ms) can be removed,and the system has a high signal-to-noise ratio, calculation volume ofthe system can be decreased, repeated calculation of the system can beavoided, the time complexity can be reduced, thereby improvingcalculation efficiency and calculation accuracy of the system.

It should be noted that the above description of the process 1000 is forexample and illustration purposes only, and does not limit the scope ofapplication of the present disclosure. For those skilled in the art,various amendments and changes may be made to process 1000 under theguidance of the present disclosure. For example, the above process 1000is only an example where the singularity is the burr signal, and whenthe singularity is a trough signal, each of the above steps (e.g., step1010, step 1020, step 1030, etc.) and the technical schemes may beadjusted or other methods may be used to perform signal correctionprocessing. However, these amendments and changes remain within thescope of the present disclosure.

In some embodiments, the signal correction processing on the singularityof the electromyographic signal may further be performed by the othermethods, e.g., a high-pass method, a low-pass method, a band-passmethod, a wavelet transform reconstruction method, etc. In someembodiments, for an application scenario where a low-frequency signal isnot sensitive, a 100 Hz high-pass filter may be used for a removal ofthe burr signal. In some embodiments, in addition to the signalcorrection processing of the electromyographic signal, the other methodsof the signal processing of the electromyographic signal, such as afiltering processing, a signal amplification, a phase adjustment, etc.,may also be performed. In some embodiments, the electromyographic signalof the user collected by the electromyographic sensor may be convertedinto a digital electromyographic signal by an analog-to-digitalconverter (ADC), and the converted digital electromyographic signal maybe subjected to a filtering process, which can filter out an industrialfrequency signal and its harmonic signal, etc. In some embodiments, theprocessing of the electromyographic signal may further include removingmotion artifacts of the user. The motion artifacts here refer to signalnoise generated by a relative movement of the muscles at the position tobe measured relative to the electromyographic module during an obtainingprocess of the electromyographic signal while the user in motion.

In some embodiments, the attitude signal may be obtained by the attitudesensor on the wearable device 130. The attitude sensor on the wearabledevice 130 may be distributed on the limb areas (e.g., arms, legs,etc.), the trunk areas (e.g., chest, abdomen, back, waist, etc.), andthe head, etc. The attitude sensor enables the collection of theattitude signal from other parts of the body such as limb parts andtrunk parts. In some embodiments, the attitude sensor may be a sensor ofan Attitude and heading reference system (AHRS) with an attitude fusionalgorithm. The attitude fusion algorithm may fuse data from a nine-axisinertial measurement unit (IMU) with a three-axis acceleration sensor, athree-axis angular velocity sensor, and a three-axis geomagnetic sensorinto Euler angles or quaternions to obtain the attitude signal of theuser's body part where the attitude sensor is located. In someembodiments, the processing module 220 and/or the processing device 110may determine the feature information corresponding to the attitudebased on the attitude signal. In some embodiments, the featureinformation corresponding to the attitude signal may include, but is notlimited to, the angular velocity value, the direction of angularvelocity, the acceleration value of angular velocity, etc. In someembodiments, the attitude sensor may be a strain sensor, and the strainsensor may obtain a bending direction and bending angle at the user'sjoints, thereby obtaining the attitude signal during the user's motion.For example, the strain sensor may be set at the knee joint of the user,and when the user is in motion, the user's body part acts on the strainsensor, and the bending direction and the bending angle at the kneejoint of the user may be calculated based on the change in resistance orlength of the strain sensor, thereby obtaining the attitude signal ofthe user's leg. In some embodiments, the attitude sensor may alsoinclude a fiber optic sensor, and the attitude signal may be representedby a change in direction after bending of a fiber from the fiber opticsensor. In some embodiments, the attitude sensor may also be a magneticflux sensor, and the attitude signal may be represented bytransformation of the magnetic flux. It should be noted that the type ofattitude sensor is not limited to the above sensors, but can also beother sensors, the sensors that can obtain the user's attitude signalare within the scope of the attitude sensor of the present disclosure.

FIG. 11 is a flowchart of an exemplary process for determining featureinformation corresponding to an attitude signal according to someembodiments of the present disclosure. As shown in FIG. 11 , the process1100 may include:

Step 1110, obtaining a target coordinate system and a conversionrelationship between the target coordinate system and at least oneoriginal coordinate system.

In some embodiments, the step may be performed by the processing module220 and/or the processing device 110. In some embodiments, the originalcoordinate system is a coordinate system corresponding to the attitudesensor set on the human body. When the user uses the wearable device130, each attitude sensor on the wearable device 130 is distributed ondifferent parts of the human body, so that installation angles of theattitude sensors are different, and the attitude sensors in differentparts use their own coordinate systems as the original coordinatesystems, so the attitude sensors in different parts have differentoriginal coordinate systems. In some embodiments, an obtained attitudesignal of the each attitude sensor may be represented in itscorresponding original coordinate system. By transforming the attitudesignal in different original coordinate systems into a same coordinatesystem (e.g., the target coordinate system), it is easy to determinerelative motion between different parts of the human body. In someembodiments, the target coordinate system refers to a human coordinatesystem established based on the human body. For example, a lengthdirection of the human torso (i.e., a direction perpendicular to atransverse plane of the body) can be used as the Z-axis, ananterior-posterior direction of the human torso (i.e., the directionperpendicular to the coronal plane of the body) as the X-axis, and theleft-right direction of the human torso (i.e., the directionperpendicular to the sagittal plane of the body) as the Y-axis in thetarget coordinate system. In some embodiments, there is a conversionrelationship between the target coordinate system and the originalcoordinate system by which the coordinate information in the originalcoordinate system can be converted to the coordinate information in thetarget coordinate system. In some embodiments, the conversionrelationship may be expressed as one or more rotation matrices. Moredescriptions of determining the conversion relationship between thetarget coordinate system and the original coordinate system may be foundin FIG. 13 of the present disclosure and its relevant descriptions.

Step 1120, converting, based on the conversion relationship, thecoordinate information in the at least one original coordinate system tothe coordinate information in the target coordinate system.

In some embodiments, the step may be performed by the processing module220 and/or the processing device 110. The coordinate information in theoriginal coordinate system is three-dimensional coordinate informationin the original coordinate system. The coordinate information in thetarget coordinate system is the three-dimensional coordinate informationin the target coordinate system. Merely as way of exemplaryillustration, the coordinate information v₁ in the original coordinatesystem may be converted to the coordinate information v₂ in the targetcoordinate system according to the conversion relationship.Specifically, a conversion between the coordinate information v₁ and thecoordinate information v₂ may be performed by using a rotation matrix,the rotation matrix here can be understood as the conversionrelationship between the original coordinate system and the targetcoordinate system. Specifically, the coordinate information v₁ in theoriginal coordinate system may be converted to coordinate informationv₁−1 by a first rotation matrix, the coordinate information v₁−1 may beconverted to coordinate information v₁−2 by a second rotation matrix,and the coordinate information v₁−2 may be converted to coordinateinformation v₁−3 by a third rotation matrix. The coordinate informationv₁−3 is the coordinate information v₂ in the target coordinate system.It should be noted that the rotation matrices are not limited to theabove first rotation matrix, the second rotation matrix and the thirdrotation matrix, but may also include fewer or more rotation matrices.In some alternative embodiments, the rotation matrix may be a rotationmatrix or a combination of a plurality of rotation matrices.

Step 1130, determining, based on the coordinate information in thetarget coordinate system, the feature information corresponding to theattitude signal.

In some embodiments, the step may be performed by the processing module220 and/or the processing device 110. In some embodiments, determining,based on the coordinate information in the target coordinate system, thefeature information corresponding to the attitude signal comprisesdetermining based on a plurality of coordinate information in the targetcoordinate system of the user during motion, the feature informationcorresponding to the attitude signal of the user. For example, when theuser performs a seated chest press, the user's arm may correspond to thefirst coordinate information in the target coordinate system when theuser's arm is held forward, and the user's arm can correspond to thesecond coordinate information in the target coordinate system when theuser's arm is opened in a same plane as the torso. Based on the firstcoordinate information and the second coordinate information, thefeature information (for example, the angular velocity, the angularvelocity direction, and the acceleration value of the angular velocity)corresponding to the attitude signal may be calculated.

It should be noted that the above description of the process 1100 is forexample and illustration purposes only and does not limit the scope ofapplication of the present disclosure. For those skilled in the art,various amendments and changes can be made to process 1100 under theguidance of the present disclosure. However, these amendments andchanges remain within the scope of the present disclosure.

In some embodiments, the relative motion between different motion partsof the user's body may be determined by the feature informationcorresponding to the attitude sensors located at the different motionparts of the user's body. For example, by using the feature informationcorresponding to the attitude sensor at the user's arm and the featureinformation corresponding to the attitude sensor at the user's torso,the relative motion between the user's arm and torso during motion canbe determined. FIG. 12 is a flowchart of an exemplary process fordetermining relative motion between the different motion parts of theuser according to some embodiments of the present disclosure. As shownin FIG. 12 , the process 1200 may include:

Step 1210, determining, based on the conversion relationships betweenthe different original coordinate systems and the target coordinatesystem, the feature information corresponding to at least two sensorsrespectively.

In some embodiments, the step may be performed by the processing module220 and/or the processing device 110. In some embodiments, differentsensors have different conversion relationships between the originalcoordinate systems corresponding to the sensors and the targetcoordinate system due to the different installation positions at thehuman body. In some embodiments, the processing device 110 may convertthe coordinate information in the original coordinate systemscorresponding to the sensors of different parts of the user (e.g., smallarm, large arm, torso, etc.) to the coordinate information in the targetcoordinate system, respectively, so that the feature informationcorresponding to at least two sensors can be determined respectively.More descriptions of the conversion of the coordinate information in theoriginal coordinate system to coordinate information in the targetcoordinate system may be found elsewhere in the present disclosure,e.g., FIG. 11 , which will not be repeated herein.

Step 1220, determining, based on the feature information correspondingto the at least two sensors respectively, the relative motion betweenthe different motion parts.

In some embodiments, the step may be performed by the processing module220 and/or the processing device 110. In some embodiments, a motion partmay refer to a limb on the human body that can move independently, forexample, a small arm, a large arm, a small leg, a thigh, etc. Merely asway of exemplary illustration, when the user performs an arm liftingdumbbell, the coordinate information in the target coordinate systemcorresponding to the sensor set at the small arm part and the coordinateinformation in the target coordinate system corresponding to the sensorset at the large arm part are combined to determine the relative motionbetween the small arm and the large arm of the user, thereby determiningthe arm lifting dumbbell movement of the user.

In some embodiments, a same motion part of the user may be arranged witha plurality of sensors of the same or different types, and thecoordinate information in the original coordinate systems correspondingto a plurality of sensors of same or different types may be converted tothe coordinate information in the target coordinate system,respectively. For example, a plurality of sensors of the same ordifferent types can be arranged at different locations of the user'ssmall arm part, and a plurality of coordinates in the target coordinatesystem corresponding to a plurality of sensors of the same or differenttypes may simultaneously represent the movement of the user's small armpart. For example, the coordinate information in the target coordinatesystems corresponding to a plurality of sensors of the same type can beaveraged, thereby improving the accuracy of the coordinate informationof the motion parts during the user's motion. For example, thecoordinate information in the target coordinate system can be obtainedby performing a fusion algorithm (e.g., Kalman filtering, etc.) on thecoordinate information in coordinate systems corresponding to aplurality of the different types of sensors.

It should be noted that the above description of the process 1100 is forexample and illustration purposes only and does not limit the scope ofapplication of the present disclosure. For those skilled in the art,various amendments and changes can be made to process 1100 under theguidance of the present disclosure. However, these amendments andchanges remain within the scope of the present disclosure.

FIG. 13 is a flowchart of an exemplary process for determining aconversion relationship between an original coordinate system to aspecific coordinate system according to some embodiments of the presentdisclosure. In some embodiments, the process of determining theconversion relationship between the original coordinate system to aspecific coordinate system may also be called a calibration process. Asshown in FIG. 13 , the process 1300 may include:

Step 1310, constructing the specific coordinate system.

In some embodiments, the step may be performed by the processing module220 and/or the processing device 110. In some embodiments, theconversion relationship between at least one original coordinate systemand the target coordinate system may be obtained by the calibrationprocess. The specific coordinate system is a reference coordinate systemconfigured to determine the conversion relationship between the originalcoordinate system and the target coordinate system during thecalibration process. In some embodiments, a constructed specificcoordinate system may have the length direction of the torso when thehuman body is standing as the Z-axis, the front-to-back direction of thehuman body as the X-axis, and the left-to-right direction of the humantorso as the Y-axis. In some embodiments, the specific coordinate systemis related to the orientation of the user during the calibrationprocess. For example, if the user's body is facing a fixed direction(e.g., north) during the calibration process, the direction in front ofthe body (north) is the X-axis.

Step 1320, obtaining the first coordinate information in the at leastone original coordinate system when the user is in a first pose.

In some embodiments, the step may be performed by the obtaining module210. The first pose may be a pose that the user approximately remainsstanding. The obtaining module 210 (e.g., the sensor) may obtain thefirst coordinate information in the original coordinate system based onthe user's first pose.

Step 1330, obtaining the second coordinate information in the at leastone original coordinate system when the user is in a second pose.

In some embodiments, the step may be performed by the obtaining module210. The second pose may be a pose that the user's body part (e.g., arm)where the sensor is located is tilted forward. In some embodiments, theobtaining module 210 (e.g., the sensor) may obtain the second coordinateinformation in the original coordinate system based on the user's secondpose (e.g., a forward leaning pose).

Step 1340, determining, according to the first coordinate information,the second coordinate information, and the specific coordinate system,the relationship between the at least one original coordinate system andthe specific coordinate system.

In some embodiments, the step may be performed by the processing module220 and/or processing device 110. In some embodiments, the firstrotation matrix may be determined through the first coordinateinformation corresponding to the first pose. In the first pose, sincethe Euler angle in a X and Y direction of the specific coordinate systemin a ZYX rotation order are 0, and the Euler angle in the X and Ydirection of the original coordinate system is not necessarily 0, thenthe first rotation matrix is the rotation matrix obtained by reversingthe original coordinate system around the X-axis and then around theY-axis. In some embodiments, the second rotation matrix may bedetermined through the second coordinate information of the second pose(e.g., the body part where the sensor is located is tilted forward).Specifically, in the second pose, it is known that the Euler angle ofthe specific coordinate system in a Y and Z₃ direction is 0 under theZYZ rotation order, and the Euler angle of the original coordinatesystem in a Y and Z₃ direction is not necessarily 0, then the secondrotation matrix is the rotation matrix obtained by reversing theoriginal coordinate system around the Y direction and then around the Z₃direction. The conversion relationship between the original coordinatesystem and the specific coordinate system may be determined through theabove first rotation matrix and second rotation matrix. In someembodiments, when there are a plurality of original coordinate systems(sensors), the above method may be configured to determine theconversion relationship between each original coordinate system and thespecific coordinate system.

It should be noted that the above first pose is not limited to anapproximately standing pose, and the second pose is not limited to thepose that the user's body part (e.g., arm) where the sensor is locatedis tilted forward, the first and second poses herein may be approximatedas being stationary during the calibration process. In some embodiments,the first pose and/or the second pose may also be a dynamic pose duringthe calibration process. For example, the user's walking attitude is arelatively fixed attitude, the angle and angular velocity of the arms,legs and feet during walking can be extracted to recognize the movement,such as forward stride, forward arm swing and the like, and the user'sforward walking attitude can be used as the second pose in thecalibration process. In some embodiments, the second pose is not limitedto one movement, but a plurality of movements can also be extracted asthe second pose. For example, the coordinate information of a pluralityof movements may be fused to obtain a more accurate rotation matrix.

In some embodiments, the rotation matrix may be dynamically correctedduring the calibration process by using some signal processingalgorithms (e.g., using Kalman filtering algorithm) to obtain a bettertransformation matrix throughout the calibration process.

In some embodiments, machine learning algorithms, or other algorithmsmay be configured for automatic recognition of some specific movementsto update the rotation matrix in real time. For example, if the machinelearning algorithm recognizes that a current user is walking, orstanding, the calibration process is automatically started. In thiscase, the wearable device does not need an explicit calibration processanymore, and the rotation matrix is dynamically updated when the useruses the wearable device.

In some embodiments, the installation position of the attitude sensormay be relatively fixed and a rotation matrix may be preset, which canmake the recognition process of the specific movement more accurate.Further, the rotation matrix continues to be corrected as the user usingthe wearable device, so that an obtained rotation matrix is closer tothe real situation.

It should be noted that the above description of the process 1300 is forexample and illustration purposes only, and does not limit the scope ofapplication of the present disclosure. For those skilled in the art,various amendments and changes can be made to process 1300 under theguidance of the present disclosure. However, these amendments andchanges remain within the scope of the present disclosure.

FIG. 14 is a flowchart of an exemplary process for determining aconversion relationship between an original coordinate system and atarget coordinate system according to some embodiments of the presentdisclosure. As shown in FIG. 14 , the process 1400 may include:

Step 1410, obtaining the conversion relationship between the specificcoordinate system and the target coordinate system.

In some embodiments, the step may be performed by the processing module220 and/or the processing device 110. Both the specific coordinatesystem and the target coordinate system take the length direction of thehuman torso as the Z-axis, so that through the conversion relationshipbetween the X-axis of the specific coordinate system and the X-axis ofthe target coordinate system and the conversion relationship between theY-axis of the specific coordinate system and the Y-axis of the targetcoordinate system, the conversion relationship between the specificcoordinate relationship and the target coordinate system can beobtained. The principle of obtaining the conversion relationship betweenthe specific coordinate relationship and the target coordinate systemmay be found in FIG. 13 and its relevant descriptions.

In some embodiments, the specific coordinate system may take the lengthdirection of the human torso as the Z-axis and a front-to-back directionof the human body as a calibrated X-axis. Since the front-to-backdirection of the user's body changes during motion (e.g., a turningmovement) and cannot be fixed in the calibrated coordinate system, it isnecessary to determine the coordinate system that can rotate with thebody, i.e., the target coordinate system. In some embodiments, thetarget coordinate system may change as the user's orientation changes,with the X-axis of the target coordinate system always being directly infront of the human torso.

Step 1420, determining, according to the conversion relationship betweenthe at least one original coordinate system and the specific coordinatesystem, and the conversion relationship between the specific coordinatesystem and the target coordinate system, the conversion relationshipbetween the at least one original coordinate system and the targetcoordinate system.

In some embodiments, the step may be performed by the processing module220 and/or the processing device 110. In some embodiments, theprocessing device 110 may determine the conversion relationship betweenthe at least one original coordinate system and the target coordinatesystem according to the conversion relationship between the at least oneoriginal coordinate system and the specific coordinate system determinedin the process 1300 and the conversion relationship between the specificcoordinate system and the target coordinate system determined in step1410, such that the coordinate information in the original coordinatesystem may be converted to the target coordinate information in thetarget coordinate system.

It should be noted that the above description of the process 1400 is forexample and illustration purposes only and does not limit the scope ofapplication of the present disclosure. For those skilled in the art,various amendments and changes can be made to the process 1400 under theguidance of the present disclosure. However, these amendments andchanges remain within the scope of the present disclosure.

In some embodiments, the position of the attitude sensors set on thewearable device 130 may change and/or the attitude sensors may beinstalled at different angles on the human body, so that the userperforms the same motion, the attitude data returned by the attitudesensors may have a relatively big difference.

FIG. 15A is an exemplary vector coordinate diagram illustrating Eulerangle data in an original coordinate system at a position of a small armof a human body according to some embodiments of the present disclosure.A boxed part may represent the Euler angle data (the coordinateinformation) in the original coordinate system corresponding to theposition of the small arm at the time the user does the same movement.As shown in FIG. 15A, the results of the Euler angle vector in theZ-axis direction (shown as “Z” in FIG. 15A) in the boxed part areapproximately in the range of −180° to (−80°). The results of the Eulerangle vector in the Y-axis direction (shown as “Y” in FIG. 15A)fluctuate approximately around 0°, and the results of the Euler anglevector in the X-axis direction (shown as “X” in FIG. 15A) fluctuateapproximately around −80°. A fluctuation range here may be 20°.

FIG. 15B is an exemplary vector coordinate diagram illustrating Eulerangle data in another original coordinate system at a position of asmall arm of a human body according to some embodiments of the presentdisclosure. The boxed part may represent the Euler angle data in theoriginal coordinate system corresponding to the other position of thesmall arm when the user performs the same movement (the same movement asshown in FIG. 15A). As shown in FIG. 15B, the results of the Euler anglevector in the Z-axis direction (shown as “Z′” in FIG. 15B) in the boxedsection are approximately in a range of −180° to 180°. The results ofthe Euler angle vector in the Y-axis direction (shown as “Y′” in FIG.15B) fluctuate approximately around 0°. And the results of the Eulerangle vector in the X-axis direction (shown as “X′” in FIG. 15B)fluctuate approximately around −150°. The fluctuation range here may be20°.

The Euler angle data shown in FIG. 15A and FIG. 15B are the Euler angledata (coordinate information) respectively obtained in the originalcoordinate system when the user performs the same movement at differentpositions of the human small arm (which can also be interpreted asdifferent installation angles of the attitude sensor at the human smallarm position). Comparing FIG. 15A with FIG. 15B, it can be seen that thewhen the user does the same movement, the angles at which the attitudesensor is installed on the human body are different, causing differencein the Euler angle data in the original coordinate system returned bythe attitude sensor. For example, the results of the Euler angle vectorin the Z-axis direction in FIG. 15A are approximately in the range of−180°-(−80°), and the results of the Euler angle vector in the Z-axisdirection in FIG. 15B are approximately in the range of −180°-180°,which are quite different from each other.

In some embodiments, the Euler angle data in the original coordinatesystem corresponding to sensors with different installation angles maybe converted to the Euler angle data in the target coordinate system,thereby facilitating analysis of the attitude signal of the sensors atdifferent positions. Merely as way of exemplary illustration, a linewhere the left arm is located can be abstracted as a unit vectorpointing from the elbow to the wrist, which is a coordinate value in thetarget coordinate system. The target coordinate system here includes theaxis pointing to the rear of the body as the X-axis, the axis pointingto the right side of the body as the Y-axis, and the axis pointing tothe top of the body as the Z-axis, which conforms to the right-handedcoordinate system. For example, a coordinate value [−1, 0, 0] in thetarget coordinate system indicates that the arm is held forward flat. Acoordinate value [0, −1, 0] of the target coordinate system indicatesthat the arm is held flat to the left. FIG. 16A is a curve obtainedbased on the vector coordinates in the target coordinate systemconverted from the Euler angle data of the small arm in the originalcoordinates in FIG. 15A. The boxed portion can represent the Euler angledata in the target coordinate system at the position of the small armwhen the user performs the movement. As shown in FIG. 16A, a small armvector [x, y, z] in the boxed portion moves reciprocally between thefirst position and the second position, where a first position is [0.2,−0.9, −0.38] and the second position is [0.1, −0.95, −0.3]. It should benoted that for each reciprocal movement of the small arm, there will bea small deviation between the first position and the second position.

FIG. 16B is an exemplary vector coordinate diagram of Euler angle datain a target coordinate system at another location of a small arm of ahuman body according to some embodiments of the present disclosure. FIG.16B is a curve obtained based on the vector coordinates in the targetcoordinate system converted from Euler angle data of the small arm inthe original coordinates in FIG. 15B. The boxed part may represent theEuler angle data in the target coordinate system at another location ofthe small arm when the user performs the same movement (the samemovement as the movement shown in FIG. 16A). As shown in FIG. 16B, asmall arm vector [x, y, z] similarly reciprocates between the firstposition and the second position, where a first position is [0.2, −0.9,−0.38] and a second position is [0.1, −0.95, −0.3].

Combining FIG. 15A to FIG. 16B, it can be seen from FIGS. 15A and 15Bthat the Euler angles in the original coordinate system have a greatdifference in the range of values and fluctuation forms due to thedifferent installation positions of the two attitude sensors. Afterconverting the coordinate information of the original coordinate systemcorresponding to the two attitude sensors to the vector coordinatescorresponding to the target coordinate system (e.g., the vectorcoordinates in FIGS. 16A and 16B) respectively, two approximately samevector coordinates may be obtained. That is to say, the method mayensure the feature information corresponding to the attitude signal tobe independent of the sensor installation position. Specifically, inFIG. 16A and FIG. 16B, it can be seen that the two attitude sensors areinstalled in different positions on the small arm, and after the abovecoordinate conversion, the same vector coordinates are obtained, i.e.,during the process of the seated chest press, they can represent theprocess of switching back and forth between the two states, state 1 (armheld flat to the right) and state 2 (arm held flat to the front).

FIG. 17 is an exemplary vector coordinate diagram of a limb vector in atarget coordinate system according to some embodiments of the presentdisclosure. As shown in FIG. 17 , the vector coordinates of the attitudesensors in the target coordinate system at the positions of the leftsmall arm (17-1), right small arm (17-2), left large arm (17-3), rightlarge arm (17-4), and torso (17-5) of the human body can be representedfrom top to bottom, respectively. The vector coordinates of eachposition (e.g., 17-1, 17-2, 17-3, 17-4, 17-5) in the target coordinatesystem of human during motion are illustrated in FIG. 17 . The first4200 points in FIG. 17 correspond to the calibration movements needed tocalibrate the limbs, such as standing, torso forward, arm forward, armside planks, etc. To use the first 4200 points corresponding to thecalibration movements to calibrate, raw data collected by the attitudesensors may be converted to the Euler angles in the target coordinatesystem. To facilitate performing analysis on the data, the coordinatevector of the arm vector in the target coordinate system may be furtherconverted. The target coordinate system here is pointing to the front ofthe torso as the X-axis, to the left of the torso as the Y-axis, and tothe top of the torso as the Z-axis. The reciprocal movements in FIG. 17are, from left to right, movement 1, movement 2, movement 3, movement 4,movement 5, and movement 6: seated chest press, high pull-down, seatedchest thrust, seated shoulder thrust, barbell dip head curl, and seatedchest press, respectively. As can be seen in FIG. 17 , differentmovements have different movement patterns, which can be clearlyrecognized by using the limb vectors. At the same time, the samemovement also has good repeatability, for example, the movement 1 andthe movement 6 both represent the seated chest press, and the curves ofthese two movements have the good repeatability.

In some embodiments, the attitude data (e.g., the Euler angles, theangular velocities, etc.) directly output in the original coordinatesystem may be converted to the attitude data in the target coordinatesystem by the processes 1300 and 1400, so that highly consistentattitude data (e.g., Euler angles, angular velocities, limb vectorcoordinates, etc.) can be obtained.

FIG. 18A is a diagram illustrating an exemplary coordinate vector of anoriginal angular velocity according to some embodiments of the presentdisclosure. The original angular velocity may be understood as theconversion of the Euler angle data in the original coordinate systemscorresponding to the sensors with different installation angles to theEuler angle data in the target coordinate system. In some embodiments,factors such as jitter during user movement may affect the results ofthe angular velocity in the attitude data. As shown in FIG. 18A, theoriginal angular velocity shows a more obvious unsmooth curve in itsvector coordinate curve under an influence of jitter, etc. For example,a presence of an abrupt signal in the vector coordinate curve of theoriginal angular velocity makes the vector coordinate curve of theoriginal angular velocity unsmooth. In some embodiments, a jitteredangular velocity needs to be corrected to obtain a smooth vectorcoordinate curve because of an effect of jitter, etc. on the angularvelocity results. In some embodiments, the original angular velocity maybe filtered by using a 1 Hz-3 Hz low-pass filtering method. FIG. 18B isan exemplary diagram illustrating results of an angular velocity afterfiltering processing according to some embodiments of the presentdisclosure. As shown in FIG. 18B, performing a low-pass filtering from 1Hz to 3 Hz on the original angular velocity may eliminate the effect ofjitter and other effects on the angular velocity (e.g., abrupt signals),so that the vector coordinate curve corresponding to the angularvelocity is displayed smoother. In some embodiments, performing thelow-pass filtering from 1 Hz to 3 Hz on the angular velocity mayeffectively prevent the effects of jitter, etc. on the attitude data(e.g., the Euler angles, the angular velocity, etc.), which makes iteasier to follow the process of segmenting the signal. In someembodiments, the filtering process may also filter out an industrialfrequency signal and its harmonic wave signal, burr signal, etc. fromthe movement signal. It should be noted that low-pass filtering at 1Hz-3 Hz introduces time delay, which makes a movement point of theattitude signal and a movement point of a real electromyographic signalmisaligned in time. Therefore, the time delay generated during thelow-pass filtering process is subtracted from the vector coordinatecurve after the low-pass filtering processing to ensure synchronizationof the attitude signal and the electromyographic signal in time. In someembodiments, the time delay is associated with a center frequency of thefilter, and when the attitude signal and the electromyographic signalare processed with different filters, and the time delay is adaptedaccording to the center frequency of the filter. In some embodiments,since the angular range of the Euler angle is [−180°, +180° ], anobtained Euler angle may have a change of −180° to +180° or +180° to−180° when an actual Euler angle is not in this angular range. Forexample, when the angle is −181°, the Euler angle changes to 179°. Inthe practical application the angle change can affect the calculation ofthe angle difference, and it is necessary to correct the angle changefirst.

In some embodiments, a movement recognition model may also be configuredto analyze the user's movement signal or the feature informationcorresponding to the movement signal to recognize the user's movement.In some embodiments, the movement recognition model includes a trainedmachine learning model configured to recognize the user's movement. Insome embodiments, the movement recognition model may include one or moremachine learning models. In some embodiments, the movement recognitionmodel may include, but is not limited to, one or more of a machinelearning model that classifies the user's movement signal, a machinelearning model that recognizes the movement quality of the user, amachine learning model that recognizes the number of the user'smovements, and a machine learning model that recognizes a fatigue indexof the user performing the movement. In some embodiments, the machinelearning model may include one or more of a linear classification model(LR), a support vector machine model (SVM), a plain Bayesian model (NB),a K-nearest neighbor model (KNN), a decision tree model (DT), ae randomforest/a gradient boosting decision tree (RF/GDBT, etc.), etc. Moredescriptions regarding the movement recognition model may be foundelsewhere in the present disclosure, such as FIG. 20 and its relevantdescriptions.

FIG. 19 is a flowchart illustrating an exemplary motion monitoring andfeedback method according to some embodiments of the present disclosure.As shown in FIG. 19 , the process 1900 may include:

Step 1910, obtaining the movement signal of the user during motion.

In some embodiments, the step may be performed by the obtaining module210. In some embodiments, the movement signal includes at least thefeature information corresponding to the electromyographic signal andthe feature information corresponding to the attitude signal. Themovement signal refers to human body parameter information of the userduring motion. In some embodiments, the human body parameter informationmay include, but is not limited to, one or more of the electromyographicsignals, the attitude signals, the heart rate signals, the temperaturesignals, the humidity signals, the blood oxygen concentration, etc. Insome embodiments, the movement signal may include at least theelectromyographic signal and the attitude signal. In some embodiments,the electromyographic sensor in the obtaining module 210 may collect theelectromyographic signal of the user during motion, and the attitudesensor in the obtaining module 210 may collect the attitude signal ofthe user during motion.

Step 1920, monitoring, based on the movement signal, the user's movementduring motion through the movement recognition model, and giving, basedon the output of the movement recognition model, the movement feedback.

In some embodiments, the step may be performed by the processing module220 and/or the processing device 110. In some embodiments, the output ofthe movement recognition model may include, but is not limited to, oneor more of the movement type, the movement quality, the number ofmovements, a fatigue index, etc. For example, the movement recognitionmodel may recognize the user's movement type as the seated chest pressbased on the movement signal. For another example, one machine learningmodel of the movement recognition model may first recognize the user'smovement type as the seated chest press based on the movement signal,and another machine learning model of the movement recognition model mayoutput the movement quality of the user's movement as a standardmovement or an incorrect movement according to the movement signal(e.g., amplitude information of the electromyographic signal, thefrequency information, and/or the angular velocity, the angular velocitydirection, and the acceleration value of angular velocity of theattitude signal). In some embodiments, the movement feedback may includesending the prompt message. In some embodiments, the prompt message mayinclude, but is not limited to, the voice prompt, the message prompt,the image prompt, the video prompt, etc. For example, if the outputresult of the movement recognition model is the incorrect movement, theprocessing device 110 may control the wearable device 130 or the mobileterminal device 140 to send a voice prompt (e.g., a message such as“nonstandard movement”) to the user to remind the user to adjust thefitness movement in a timely manner. For another example, if the outputof the movement recognition model is the standard movement, the wearabledevice 130 or the mobile terminal device 140 may not send a promptmessage, or send a prompt message like “standard movement”. In someembodiments, the motion feedback may also include the wearable device130 stimulating the corresponding movement part of the user. Forexample, the components of the wearable device 130 stimulate thecorresponding parts of the user's movements through a vibrationfeedback, an electrical stimulation feedback, a pressure feedback, etc.For example, the output results of the movement recognition model arethe incorrect movement, and the processing device 110 may control thecomponents of the wearable device 130 to stimulate the correspondingparts of the user's movement. In some embodiments, the movement feedbackmay also include outputting a motion record of the user during motion.The motion record here may refer to one or more of the user's movementtype, exercise duration, number of movements, movement quality, fatigueindex, physiological parameter information during motion, etc.Descriptions regarding the movement recognition model may be foundelsewhere in the present disclosure and will not be repeated herein.

It should be noted that the above description of the process 1900 is forexample and illustration purposes only and does not limit the scope ofapplication of the present disclosure. For those skilled in the art,various amendments and changes can be made to process 1900 under theguidance of the present disclosure. However, these amendments andchanges remain within the scope of the present disclosure.

FIG. 20 is a flowchart illustrating an exemplary process for modeltraining according to some embodiments of the present disclosure.

In step 2010, obtaining the sample information.

In some embodiments, the step may be performed by the obtaining module210. In some embodiments, the sample information may include themovement signal of professionals (e.g., fitness trainers) and/ornon-professionals during motion. For example, the sample information mayinclude the electromyographic signals and/or the attitude signalsgenerated by the professionals and/or the non-professionals whileperforming the same movement type (e.g., the seated chest press). Insome embodiments, the electromyographic signal and/or attitude signal inthe sample information may undergo the segmentation processing of theprocess 700, the burr processing of the process 900, and the conversionprocessing of the process 1300, etc., to form at least one segment ofthe electromyographic signal and/or the attitude signal. The at leastone segment of the electromyographic signal and/or the attitude signalmay be used as the input of the machine learning model to train themachine learning model. In some embodiments, the feature informationcorresponding to the at least one segment of the electromyographicsignal and/or the feature information corresponding to the attitudesignal may also be used as the input of the machine learning model totrain the machine learning model. For example, the frequency informationand the amplitude information of the electromyographic signal can beused as the input of the machine learning model. For another example,the angular velocity of the attitude signal and the angular velocitydirection/the acceleration value of the angular velocity can be used asthe input of the machine learning model. For another example, themovement start point, the movement middle point and the movement endpoint signal can be used as the inputs to the machine learning model. Insome embodiments, the sample information may be obtained from thestorage device of the processing device 110. In some embodiments, thesample information may be obtained from the obtaining module 210.

In step 2020, training the movement recognition model.

The step may be performed by the processing device 110. In someembodiments, the movement recognition model may include one or moremachine learning models. For example, the movement recognition model mayinclude, but is not limited to, one or more of the machine learningmodel that classifies the user's movement signal, the machine learningmodel that recognizes the movement quality of the user, the machinelearning model that recognizes the number of user's movement, and themachine learning model that recognizes the fatigue level of the userperforming the movement. In some embodiments, the machine learning modelmay include one or more of the linear classification model (LR), thesupport vector machine model (SVM), the Native Bayesian model (NB), theK-nearest neighbor model (KNN), the decision tree model (DT), the randomforest/the gradient boosting decision tree (RF/GDBT, etc.), etc.

In some embodiments, training of the machine learning model may includeobtaining the sample information. In some embodiments, the sampleinformation may include the movement signal of the professionals (e.g.,fitness trainers) and/or non-professionals during motion. For example,the sample information may include electromyographic signal and/orpostural signal generated by professionals and/or the non-professionalswhile performing the same movement type (e.g., the seated chest press).In some embodiments, the electromyographic signal and/or the attitudesignal in the sample information may undergo the segmentation processingof the process 700, the burr processing of the process 900, and theconversion processing of the process 1300, etc., to generate at leastone segment of the electromyographic signal and/or the attitude signal.The at least one segment of the electromyographic signal and/or theattitude signal may be used as the input to the machine learning modelto train the machine learning model. In some embodiments, the featureinformation corresponding to the at least one segment of theelectromyographic signal and/or the feature information corresponding tothe attitude signal may also be used as the input of the machinelearning model to train the machine learning model. For example, thefrequency information and the amplitude information of theelectromyographic signal can be used as the input of the machinelearning model. For another example, the angular velocity of theattitude signal and the angular velocity direction/acceleration value ofthe velocity angle can be used as the input of the machine learningmodel. For another example, the signal corresponding to the movementstart point, the movement middle point, and/or the movement end pointsignal (including the electromyographic signal and/or the attitudesignal) can be used as the input of the machine learning model.

In some embodiments, when training a machine learning model forrecognizing the user's movement type, the sample information from thedifferent movement types (per segment of the electromyographic signalor/and the attitude signal) may be labelled and processed. For example,the sample information from the electromyographic signal and/or theattitude signal generated by the user performing a seated chest pressmay be marked as “1”, where “1” is configured to represent the “seatedchest press”. The sample information from the electromyographic signaland/or the attitude signal generated when the user performs the biceplifting maybe marked as “2”, where “2” is configured to represent the“bicep lifting”. The different movement types correspond to thedifferent feature information (e.g., the frequency information, theamplitude information) of electromyographic signals, and featureinformation (e.g., angular velocity, angular velocity direction, angularvelocity value of angular velocity) of attitude signals. Labeled sampleinformation (e.g., feature information corresponding toelectromyographic signal and/or attitude signal in the sampleinformation) is used as the input of the machine learning model to trainthe machine learning model, so that the movement recognition modelconfigured to recognize the user's movement type may be obtained, and byinputting the movement signal in the machine learning model, acorresponding movement type may be output.

In some embodiments, the movement recognition model may further includethe machine learning model for determining the quality of the user'smovement. The sample information here may include both the standardmovement signal (also known as a positive sample) and a non-standardmovement signal (also known as negative samples). The standard movementsignal may include the movement signal generated by the professionalperforming the standard movement. For example, the movement signalgenerated by a professional performing the standard seated chest pressis the standard movement signal. The non-standard movement signal mayinclude the movement signal generated by the user performing thenon-standard movement (e.g., an incorrect movement). In someembodiments, the electromyographic signal and/or the attitude signal inthe sample information may undergo the segmentation processing of theprocess 700, the burr processing of the process 900, and the conversionprocessing of the process 1300, etc., to generate at least one segmentof the electromyographic signal and/or the attitude signal. The at leastone segment of the electromyographic signal and/or the attitude signalmay be used as the input of the machine learning model to train themachine learning model. In some embodiments, the positive and negativesamples of the sample information (per segment of the electromyographicsignal or/the attitude signal) may be tagged. For example, a positivesample is marked as “1” and a negative sample is marked as “0”. The “1”here is configured to represent the user's movement as a standardmovement, and the “0” here is configured to represent the user'smovement as a wrong movement. The trained machine learning model mayoutput different labels based on the input sample information (e.g., thepositive sample, the negative sample). It should be noted that themovement recognition model may include one or more machine learningmodels for analyzing and recognizing the quality of the user movement,and different machine learning models may analyze and recognize thesample information from the different movement types, respectively.

In some embodiments, the movement recognition model may also include amodel that recognizes the number of movements of the user's fitnessmotion. For example, the movement signal (e.g., the electromyographicsignal and/or the attitude signal) in the sample information issegmented by the process 700 to obtain at least one set of a movementstart point, a movement middle point, and a movement end point, and eachset of the movement start point, the movement middle point, and themovement end point is marked, for example, the movement start point ismarked as 1, the movement middle point is marked as 2, and the movementend point is marked as 3, and the marks are used as the input to themachine learning model, and a set of consecutive “1”, “2” and “3” areinput to the machine learning model to output one movement. For example,three consecutive sets of “1”, “2”, and “3” are input into a machinelearning model to output three movements.

In some embodiments, the movement recognition model may also include amachine learning model for identifying a user's fatigue index. Thesample information here may also include signals of other physiologicalparameters such as the electro-cardio signals, the respiratory rates,the temperature signals, the humidity signals, etc. For example,different frequency ranges of the electro-cardio signal can be used asthe input data for the machine learning model, with electro-cardiosignal in the range of 60 beats/min-100 beats/min marked as “1” (normal)and less than 60 beats/min or more than 100 beats/min marked as “2”(abnormal). In some embodiments, a further segmentation can be performedand different indices can be labeled as the input data based on theuser's electro-cardio signal frequency, and the trained machine learningmodel can output a corresponding fatigue index according to thefrequency of the electro-cardio signal. In some embodiments, the machinelearning model may also be trained in conjunction with the physiologicalparameter signal such as the respiratory rate and the temperaturesignal. In some embodiments, the sample information may be obtained fromthe storage device of processing device 110. In some embodiments, thesample information may be obtained from the obtaining module 210. Itshould be noted that the movement recognition model can be any one ofthe above machine learning models or a combination of a plurality ofabove machine learning models, or include other machine learning models,which can be selected according to the actual situation. In addition, atraining input to the machine learning model is not limited to onesegment (one cycle) of the movement signal, but can also be part of asegment of the movement signal, or a plurality of segments of themovement signal, etc.

Step 2030, extracting the movement recognition model.

In some embodiments, the step may be performed by the processing device110. In some embodiments, the processing device 110 and/or theprocessing module 220 may extract the movement recognition model. Insome embodiments, the movement recognition model may be stored to theprocessing device 110, the processing module 220, or the mobileterminal.

Step 2040, obtaining the user's movement signal.

In some embodiments, the step may be performed by the obtaining module210. For example, in some embodiments, the electromyographic sensor inthe obtaining module 210 may obtain the electromyographic signal of theuser, and the attitude sensor in the obtaining module 210 may obtain theattitude signal of the user. In some embodiments, the user movementsignal may also include other physiological parameter signals such asthe electro-cardio signal, the respiration signal, the temperaturesignal, the humidity signal, etc. of the user during motion. In someembodiments, the obtained movement signal (e.g., the electromyographicsignal and/or the attitude signal) may be subjected to the segmentationprocessing of the process 700, the burr processing of process the 900,and the conversion processing of the process 1300, etc., to form atleast one segment of the electromyographic signal and/or the attitudesignal.

Step 2050, judging, based on the user's movement signal, the movementthrough the movement recognition model.

The step may be performed by the processing device 110 and/or theprocessing module 220. In some embodiments, the processing device 110and/or the processing module 220 may determine the user movement basedon the movement recognition model. In some embodiments, the trainedmovement recognition model may include one or more machine learningmodels. In some embodiments, the movement recognition model may include,but is not limited to, one or more of the machine learning model thatclassifies the user's movement signal, the machine learning model thatrecognizes the movement of the user, the machine learning model thatrecognizes the number of user's movement, and the machine learning modelthat recognizes the fatigue index of the user performing the movements.The different machine learning models may have different recognitioneffects. For example, a machine learning model for classifying themovement signal may use the user's movement signal as input data andoutput the corresponding movement type. For example, a machine learningmodel that recognizes the quality of the user's movement can use theuser's movement signal as input data and output the movement quality(e.g., standard movement, wrong movement). For example, the machinelearning model that recognizes the fatigue index of a user performing amovement can use the user's movement signal (e.g., the electro-cardiosignal frequency) as the input data and output the user's fatigue index.In some embodiments, the user's movement signal and the judgment results(output) of the machine learning model may also be used as the sampleinformation of training the movement recognition model to optimizerelevant parameters of the movement recognition model. It should benoted that the movement recognition model is not limited to the trainedmachine learning model described above, but can also be a preset model,e.g., a manually predefined conditional judgment algorithm or add anartificially added parameter (e.g., confidence level) to the trainedmachine learning model, etc.

Step 2060, providing, based on the judgment results, feedback for theuser's movement.

In some embodiments, the step may be performed by the wearable device130 and/or the mobile terminal device 140. Further, the processingdevice 110 and/or the processing module 220 sends a feedback instructionto the wearable device 130 and/or the mobile terminal device 140 basedon the judgment results of the user's movement, and the wearable device130 and/or the mobile terminal device 140 provides the feedback to theuser based on the feedback instruction. In some embodiments, thefeedback may include sending prompt messages (e.g., text information,picture information, video information, voice information, indicatorinformation, etc.) and/or stimulating the user's body when performingthe movement (in a form of electrical stimulation, vibration, pressurechanges, heat change, etc.). For example, when a user performs a sit-upmovement, the user's movement signal is monitored and it is determinedthat the user is exerting too much force on the oblique muscles duringmotion (i.e., a user's head and neck movement are not standard), inwhich case the input/output module 260 (e.g., a vibration prompter) inthe wearable device 130 and the mobile terminal device 140 (e.g., asmartwatch, smartphone etc.) provide a corresponding feedback (e.g.,perform the vibration on the user's body part, send the voice prompt,etc.) to prompt the user to adjust the force-exerting part of body intime. In some embodiments, during the user's movement, by monitoring themovement signal during the user's movement and determining the movementtype, the movement quality, and the number of the user's movementsduring motion, the mobile terminal device 140 can output thecorresponding movement record so that the user can understand its motionsituation during motion.

In some embodiments, when the feedback is given to the user, thefeedback may be matched to user perception. For example, if the user'smovement is not standard, the user can know that the movement is notstandard based on the vibration stimulation in the area corresponding tothe user's movement, and the vibration stimulation is in an acceptablerange of the user. Further, a matching model may be constructed based onuser's movement signal and the user perception to find the best balancebetween the user perception and a real feedback.

In some embodiments, the movement recognition model may further betrained based on the user's movement signals. In some embodiments,training the movement recognition model according to the user's movementsignal may include evaluating the user's movement signal to determine aconfidence level of the user's movement signal. The confidence level mayindicate the quality of the user's movement signal. For example, thehigher the confidence level, the better the quality of the user'smovement signal. In some embodiments, evaluation of the user's movementsignal may be performed at the stages of the movement signal obtaining,pre-processing, segmentation, and/or recognition.

In some embodiments, training the movement recognition model accordingto the user's movement signal may further include determining whetherthe confidence level is greater than a confidence level threshold (e.g.,80), and if the confidence level is greater than or equal to theconfidence level threshold, the movement recognition model is trained byusing the user's movement signal corresponding to that confidence levelas sample data. If the confidence level is less than the confidencelevel threshold, the user's movement signal corresponding to theconfidence level is not used as sample data to train the movementrecognition model. In some embodiments, the confidence level mayinclude, but is not limited to, a confidence level at any of the stagesof the movement signal obtaining, the movement signal pre-processing,movement signal segmentation, or the movement signal recognition. Forexample, the confidence level of the movement signal collected by theobtaining module 210 is used as a judgment criterion. In someembodiments, the confidence level may further be a combined confidencelevel at any of the above stages. The combined confidence level may beobtained by averaging or weighting the confidence levels of the stages,etc. In some embodiments, the movement recognition model according tothe user's movement signal may be trained in real time, periodically(e.g., a day, a week, a month, etc.), or when a certain data size ismet.

It should be noted that the above description of the process 1700 ismerely provided for the purpose of illustration, and is not intended tolimit the scope of the present disclosure. For those skilled in the art,various of amendments and changes may be made to the process 1700 underthe guidance of the present disclosure. However, these amendments andchanges remain within the scope of this disclosure.

The basic concepts have been described. Obviously, for those skilled inthe art, the detailed disclosure may be only an example and does notconstitute a limitation to the present disclosure. Although notexplicitly stated here, those skilled in the art may make variousmodifications, improvements, and amendments to the present disclosure.These alterations, improvements, and modifications are intended to besuggested by this disclosure, and are within the spirit and scope of theexemplary embodiments of this disclosure.

Moreover, certain terminology has been used to describe embodiments ofthe present disclosure. For example, the terms “one embodiment,” “anembodiment,” and/or “some embodiments” mean that a particular feature,structure or characteristic described in connection with the embodimentis included in at least one embodiment of the present disclosure.Therefore, it is emphasized and should be appreciated that two or morereferences to “an embodiment” or “one embodiment” or “an alternativeembodiment” in various parts of the present disclosure are notnecessarily all referring to the same embodiment. In addition, somefeatures, structures, or features in the present disclosure of one ormore embodiments may be appropriately combined.

In addition, those skilled in the art can understand that variousaspects of the present disclosure can be illustrated and describedthrough several patentable categories or situations, including any newand useful processes, machines, products, or combinations of materials,or any new and useful improvements. Accordingly, all aspects of thepresent disclosure may be performed entirely by hardware, may beperformed entirely by software (including firmware, resident software,microcode, etc.), or may be performed by a combination of hardware andsoftware. The above hardware or software can be referred to as “datablock”, “module”, “engine”, “unit”, “component” or “system”. Inaddition, aspects of the present disclosure may appear as a computerproduct located in one or more computer-readable media, the productincluding computer-readable program code.

The computer storage medium may include a propagation data signalcontaining a computer program encoding, such as on a baseband or as partof a carrier. The propagation signal may have a variety of expressions,including electromagnetic form, optical form, or suitable combinationform. The computer storage medium can be any computer-readable mediumother than the computer-readable storage medium, which can be used toperform systems, devices, or devices to implement communication,propagating, or devices by connecting to an instruction. The programcode located on the computer storage medium may be propagated throughany suitable medium, including radio, cable, fiber optic cable, RF, orsimilar media, or any combination of the foregoing.

Computer program code for carrying out operations for aspects of thepresent disclosure may be written in any combination of one or moreprogramming languages, including an object-oriented programming languagesuch as Java, Scala, Smalltalk, Eiffel, JADE, Emerald, C++, C #, VB.NET,Python or the like, conventional procedural programming languages, suchas the “C” programming language, Visual Basic, Fortran 2003, Perl, COBOL2002, PHP, ABAP, dynamic programming languages such as Python, Ruby, andGroovy, or other programming languages. The program code may executeentirely on the user's computer, partly on the user's computer, as astand-alone software package, partly on the user's computer, partly on aremote computer, or entirely on the remote computer or server. In thecase of subsequent cases, the remote computer can be connected to theuser computer through any network, such as a local area network (LAN) ora wide area network (WAN), or connected to an external computer (e.g.,through the Internet), or in the cloud computing environment, or as aservice Use Software, SaaS.

Moreover, unless otherwise specified in the claims, the sequence of theprocessing elements and sequences of the present disclosure, the use ofdigital letters, or other names are not used to define the order of theapplication flow and methods. Although the above disclosure discussesthrough various examples what is currently considered to be a variety ofuseful embodiments of the disclosure, it is to be understood that suchdetail is solely for that purpose and that the appended claims are notlimited to the disclosed embodiments, but, on the contrary, are intendedto cover modifications and equivalent arrangements that are within thespirit and scope of the disclosed embodiments. For example, although theimplementation of various components described above may be embodied ina hardware device, it may also be implemented as a software-onlysolution, e.g., an installation on an existing server or mobile device.

Similarly, it should be appreciated that in the foregoing description ofembodiments of the present disclosure, various features are sometimesgrouped together in a single embodiment, figure, or description thereoffor the purpose of streamlining the disclosure and aiding in theunderstanding of one or more of the various embodiments. However, thisdisclosure does not mean that the present disclosure object requiresmore features than the features mentioned in the claims. Rather, claimedsubject matter may lie in less than all features of a single foregoingdisclosed embodiment.

In some embodiments, numbers expressing quantities of ingredients,properties, and so forth, configured to describe and claim certainembodiments of the application are to be understood as being modified insome instances by the term “about,” “approximate,” or “substantially”.Unless otherwise stated, “approximately”, “approximately” or“substantially” indicates that the number is allowed to vary by ±20%.Correspondingly, in some embodiments, the value parameters used in thepresent disclosure and claims are approximate values. The approximatevalues may be changed according to the characteristics of individualembodiments. In some embodiments, the numerical parameters should beconstrued in light of the number of reported significant digits and byapplying ordinary rounding techniques. Although the numerical domainsand parameters used in the present application are used to confirm therange of ranges, the settings of this type are as accurate in thefeasible range within the feasible range in the specific embodiments.

For each patent, patent application, patent application publication, andother materials cited in the present disclosure, such as articles,books, specifications, publications, documents, etc., the entirecontents are hereby incorporated by reference into the presentdisclosure. Except for application history documents that areinconsistent with or conflict with the contents of the presentdisclosure, the documents with the most limited scope of the claims ofthe present disclosure (current or later appended to the presentdisclosure) are also excluded. It should be noted that if thedescription, definition, and/or terms used in the appended applicationof the present disclosure are inconsistent or conflicting with thecontent described in the present disclosure, the use of the description,definition, and/or terms of the present disclosure shall prevail.

At last, it should be understood that the embodiments described in thepresent disclosure are merely illustrative of the principles of theembodiments of the present disclosure. Other modifications that may beemployed may be within the scope of the present disclosure. Thus, by wayof example, but not of limitation, alternative configurations of theembodiments of the present disclosure may be utilized in accordance withthe teachings herein. Accordingly, the embodiments of the presentdisclosure are not limited to that precisely as shown and described.

1. A motion monitoring method, comprising: obtaining a movement signalof a user during motion, the movement signal comprising at least anelectromyographic signal or an attitude signal; and monitoring, at leastbased on feature information corresponding to the electromyographicsignal or feature information corresponding to the attitude signal, amovement of the user during motion, wherein the feature informationcorresponding to the electromyographic signal includes at leastfrequency information or amplitude information, and the featureinformation corresponding to the attitude signal includes at least oneof an angular velocity direction, an angular velocity value, anacceleration of an angular velocity, an angle, displacement information,and stress.
 2. The motion monitoring method of claim 1, comprising:segmenting, based on the feature information corresponding to theelectromyographic signal or the feature information corresponding to theattitude signal, the movement signal; and monitoring, based on at leastone segment of the movement signal, the movement of the user duringmotion.
 3. (canceled)
 4. The motion monitoring method of claim 3,wherein the segmenting, based on the feature information correspondingto the electromyographic signal or the feature information correspondingto the attitude signal, the movement signal includes: determining, basedon a time domain window of the electromyographic signal or the attitudesignal, at least one target feature point from the time domain windowaccording to a preset condition; and segmenting, based on the at leastone target feature point, the movement signal.
 5. The motion monitoringmethod of claim 4, wherein the at least one target feature pointincludes one of a movement start point, a movement middle point, and amovement end point.
 6. The motion monitoring method of claim 5, whereinthe preset condition includes one or more of a change in the angularvelocity direction corresponding to the attitude signal; the angularvelocity corresponding to the attitude signal being greater than orequal to an angular velocity threshold; a changed value of the angularvelocity value corresponding to the attitude signal being an extremevalue; the angle corresponding to the attitude signal reaching anangular threshold; and the amplitude information corresponding to theelectromyographic signal being greater than or equal to one or moreelectromyographic thresholds.
 7. The motion monitoring method of claim6, wherein the preset condition further includes the acceleration of theangular velocity corresponding to the attitude signal being continuouslygreater than or equal to an acceleration threshold of the angularvelocity for a first specific time range.
 8. The motion monitoringmethod of claim 6, wherein the preset condition further includes anamplitude corresponding to the electromyographic signal beingcontinuously greater than the one or more electromyographic thresholdsfor a second specific time range.
 9. The motion monitoring method ofclaim 1, wherein the monitoring, at least based on feature informationcorresponding to the electromyographic signal or feature informationcorresponding to an attitude signal, a movement of the user duringmotion comprises: pre-processing the electromyographic signal in afrequency domain or a time domain; obtaining, based on the pre-processedelectromyographic signal, the feature information corresponding to theelectromyographic signal; and monitoring, according to the featureinformation corresponding to the electromyographic signal or the featureinformation corresponding to the attitude signal, the movement of theuser during motion.
 10. The motion monitoring method of claim 9, whereinthe pre-processing the electromyographic signal in a frequency domain ora time domain includes: filtering the electromyographic signal to selectcomponents of the electromyographic signal in a specific frequency rangein the frequency domain.
 11. The motion monitoring method of claim 9,wherein the pre-processing the electromyographic signal in a frequencydomain or a time domain includes: performing a signal correctionprocessing on the electromyographic signal in the time domain.
 12. Themotion monitoring method of claim 11, wherein the performing a signalcorrection processing on the electromyographic signal in the time domainincludes: determining a singularity in the electromyographic signal,wherein the singularity corresponds to an abrupt signal of theelectromyographic signal; and performing the signal correctionprocessing on the singularity in the electromyographic signal.
 13. Themotion monitoring method of claim 12, wherein the performing the signalcorrection processing on the singularity in the electromyographic signalincludes removing the singularity or performing the signal correctionprocessing on the singularity according to a signal around thesingularity includes: removing the singularity, or correcting thesingularity according to a signal around the singularity.
 14. The motionmonitoring method of claim 12, wherein the singularity includes a burrsignal, the determining the singularity in the electromyographic signalincludes: selecting, based on the time domain window of theelectromyographic signal, different time windows from the time domainwindow of the electromyographic signal, wherein the different timewindows respectively cover different time ranges; and determining, basedon the feature information corresponding to the electromyographic signalin the different time windows, the burr signal.
 15. The motionmonitoring method of claim 1, further comprising determining, based onthe attitude signal, the feature information corresponding to theattitude signal, wherein the attitude signal comprises coordinateinformation in at least one original coordinate system; and determining,based on the attitude signal, the feature information corresponding tothe attitude signal comprises: obtaining a target coordinate system anda conversion relationship between the target coordinate system and theat least one original coordinate system; converting, based on theconversion relationship, the coordinate information in the at least oneoriginal coordinate system to coordinate information in the targetcoordinate system; and determining, based on the coordinate informationin the target coordinate system, the feature information correspondingto the attitude signal.
 16. The motion monitoring method of claim 15,wherein the attitude signal includes coordinate information generated byat least two sensors, the at least two sensors are located at differentmotion parts of the user and correspond to different original coordinatesystems, the determining, based on the attitude signal, the featureinformation corresponding to the attitude signal includes: determiningfeature information corresponding to each of the at least two sensorsbased on the conversion relationship between different originalcoordinate systems and the target coordinate system; and determining,based on the feature information respectively corresponding to the atleast two sensors, a relative motion between the motion parts of theuser.
 17. The motion monitoring method of claim 15, wherein theconversion relationship between the at least one original coordinatesystem and the target coordinate system is obtained by a calibrationprocess including: constructing a specific coordinate system, thespecific coordinate system being related to an orientation of the userduring the calibration process; obtaining first coordinate informationof the at least one original coordinate system when the user is in afirst pose; obtaining second coordinate information of the at least oneoriginal coordinate system when the user is in a second pose; anddetermining the conversion relationship between the at least oneoriginal coordinate system and the specific coordinate system accordingto the first coordinate information, the second coordinate information,and the specific coordinate system.
 18. The motion monitoring method ofclaim 17, where the calibration process further includes: obtaining aconversion relationship between the specific coordinate system and thetarget coordinate system; and determining, according to the conversionrelationship between the at least one original coordinate system and thespecific coordinate system as well as the conversion relationshipbetween the specific coordinate system and target coordinate system, theconversion relationship between the at least one original coordinatesystem and the target coordinate system.
 19. The motion monitoringmethod of claim 15, wherein the target coordinate system changes as theuser's orientation changes.
 20. (canceled)
 21. A motion monitoring andfeedback method, comprising: obtaining movement signal of a user duringmotion, wherein the movement signal includes at least anelectromyographic signal and an attitude signal; and monitoring, basedon feature information corresponding to the electromyographic signal andfeature information corresponding to the attitude signal, a movement ofa user by a movement recognition model, and providing, based on anoutput of the movement recognition model, a movement feedback. 22.(canceled)
 23. The motion monitoring and feedback method of claim 21,wherein the movement feedback includes at least one of sending a promptmessage, stimulating a movement part of the user, and outputting amotion record of the user during motion.